neural network

In practice, we still can only use artificial neural network as a copper price forecasting methods one of which should not be the only one.

And on the basis of it, a neural network optimal design method based on orthogonal experiment method is carried out.

The trained neural network model of this system, network model after training of can be able to predict water quality of the breeding ponds.

At last great amount of data were collected for training the neural network to obtain a forecasting model which had a high accuracy.

Theoretically, it could be re-created on a neural network, but that would be very difficult, as it would require all one's memories.

This caused the traditional neural network algorithm to be unable to carry on the adjustment well to the bias noise.

Multi-electrode Array has a plastic chamber, in which both brain slice and neural-network in vitro can be cultured on the surface of MEA.

Artificial Neural Network (ANN) is capable of dealing with non linear problems and of fault tolerant. It can be used for on line response.

The improved gradient descent algorithm has been used to train the wavelet neural network.

For a more relevant test of Psyco, I dug up some neural network code that I had written about in an earlier article (see Resources).

while neural network does local ones, which acts as a fine optimizing or on-line studying process.

Finally, wavelet analysis and neural network theory are used to analyze the acceleration signals to determine the direction of car crash.

Artificial neural network is one of information precessing techniques which have been developing rapidly recent years.

We believe that this artificial neural network and soft sensing technique will become part of the development in sewage treatment.

The Bayesian and neural network classification methods are widely used for automatic and supervised image segmentation.

In this paper, a new neural network consisting of a Self-organizing Feature Map Neural Network and a Tree-based Neural Network is proposed.

Conventional neural network is always complicated and its stability often suffers from the effect of initial weight value selecting.

The condition for a fuzzy neural network to realize memory is discussed, and the concept of lattice point is given.

Artificial Neural Network is one of the efficient information processing method in studying the sophisticated systems.

The total torque requirement is calculated by a nonlinear auto-regression with exogenous input (NARX) dynamic neural network model.

The procedure neural network was proposed to identify the parameters for a time-varying three degrees of freedom system in the paper.

The aim of this research is trying to solve the problem of local minimum while neural network is used in the real application.

Neural network (NN) has recently applied to mobile robot control due to its universal mapping property, and the result will be satisfactory.

Using this method, the data set of neural network model is selected, and the result of selection is primarily validated.

As a real-time QP solver, a LVIbased primal-dual neural network was developed with a simple piecewise-linear structure and highe.

This paper sets up the model of artificial neural network for acoustic direction finding systems of targets used in the battle field.

Neural network model is an effective data processing method, but it has a shortcoming of difficult structure confirmation.

Feedforward fuzzy neural network is the result of organically integrating fuzzy technology and feedforward neural network.

Neural network ensemble is the international machine learning and neural computation of a very active academic research focus.

Once be trained, the neural network model can be used during microwave design to provide instant answers to any pre-assigned task.

This paper researched on a class of compound fuzzy neural network and made a variety of improvements.

BP network is a feedforward neural network practiced by back propagation algorithm, is one of neural network modes applied widely.

Grey prediction was dominated in the method, the neural network was used for compensating the prediction.

One question, for starters, is what might happen if that "neural network" were to go haywire, or be sabotaged.

By linking the side neurons, some lines are formed. Each line becomes an internal net of the hierarchical self organizing neural network.

The authors studied the feedback neural network applied to image reconstruction from a few projections.

The Compensated Fuzzy-Neural network algorithm and the rules of the fuzzy control are applied to the system.

The neural network model contains the feedback of the state neurons and takes the form of state space representation.

It is the current research direction to set up the neural network expert system of judging the abnormal state of blast furnace .

The objective of the research is to set up the curing deformation prediction model of composites based on artificial neural network.

An improved algorithm in neural network(NN) data pretreatment was presented. By testing, the data pretreatment method is better than before.

It has much practical significance. Artificial neural network technology is a hotspot in intelligent information processing field.

Computer vision and artificial neural network technologies were synthetically applied to automatic classification of potato quality.

Neural network method was used to investigate the parison swell affected by the die temperature and extrusion flow rate.

On this basis, the artificial neural network model and system architecture of the ventilation systems evaluation is established.

A neural network was used to express the nonlinear function between input and output of the inverse kinematics of the robot.

The method can be extended to more types of neural networks, provide a reliable basis for the neural network hardware implementation .

The merits and limitations of genetic algorithms used in optimizing the connection weights of the neural network are discussed.

Furthermore, we investigate the synchronous behavior of a neural network after learning at various developing stages .

A simplified adaptive neural network (NN) stabilization algorithm is proposed for a class of output feedback nonlinear time-delay systems.

However, The knowledge learned by a trained neural network is difficult to understand.

The shell coefficients are used as features to input into a feed-forward neural network to recognize the handwritten numerals.

At last, the system modeling the uses action by Neural Network to forecast which kind of webs will the users view for some time.

Artificial neural network is an effective means for quantitative electromagnetic nondestructive testing.

Then the paper proposes a brand new neural network, termed dyadic network, and a brand new fuzzy model, termed dyadic fuzzy system.

The calculation model for predicting rubber aging properties was derived by means of neural network method.

The application example showed that the rough neural network is an efficient and feasible algorithm to discrimination of air pollution.

This paper studies Bayesian neural network in power load forecasting, load forecasting power of the basic concepts and methods.

Artificial neural network has good nonlinear mapping and a high degree of parallel processing of information capacity.

Based on a 3-layer BP artificial neural network and the layered elastic moduli was back calculated by the surface deflections.

Furthermore, an optical fiber sensor model of correcting static error based on BP artificial neural network (ANN) is set up in this paper.

A method for predicting product-yields of naphtha steam cracking was established by means of BP neural network model.

As the advantage of this neural network is introduced, some hot potatoes are also discussed.

Thermal resistance characteristics of the establishment of neural network model for automatic temperature measurement system is important.

The damped least square method was applied in both identification of the linear model and study of the neural network.

This paper simply introduces the basic theory and arithmetic of the fuzzy neural network on which the GCAQBP model established.

The experiment results show that wavelet neural network has the better ability to the fine structure difference between the spectra.

The model combines the advantage of genetic algorithms and neural network. It is of better performance, faster speed and high accurate.

The neural network has a good nonlinear function approximation capability, which can be used in NC machine error identification.

In this method a wavelet neural network model is built, through a gradient descent error algorithm, the network updates its parameters.

Finally, the damage degree is determined by radial basis function neural network with input of the change rate of squared modal frequency.

Constructs the description model of environment and obstacle with neural network. Asks for the best path with immune evolution algorithm.

An artificial neural network based expert system has been developed and is applied to aircraft turbofan engine fault diagnosis.

The simulation results show that the training time of neural network with processed samples is sho . . .

If the training data and diagnostic data used in neural network diagnosis have redundant information, it will affect the diagnostic ability.

As a fast and accurate modeling tool, neural network has been applied to electromagnetic engineering computer-aided design.

Neural network system is applied to an expert system for the compressor fault diagnosis of vehicle's air conditioning.

The application of neural network models has proven that the predicted results of structure and properties can be improved.

Parallel genetic algorithm was applied to optimization of BP neural network and its initial weights.

This paper presents a Time-Delay Recurrent Neural Network (TDRNN) for exo-atmospheric ballistic space infrared target discrimination.

Neural network was used to design suspension damping PID controllers whose parameters were optimized on line.

The neural network can be used to predict human psychological perceptions of clothing sensory comfort.

The paper presents a novel algorithm to design optimal parameters of a gray morphological filter with neural network.

The BP neural network was used to the bicycles'decision-making model, enriching the micro-model of mixed traffic flow.

This paper covers an analog compound orthogonal neural network controller and its application to two closed-loop DC speed governing system.

In addition, it does not require the time-consuming training process offline and saves the time of neural network training.

A neural network typically consists of a number of interconnected processors, or nodes.

Neural network methods to determine the optimum excess air coefficient are tried to be used.

To describe the complex relations more reasonably, the artificial neural network (ANN) theory is applied to spot welding process modeling.

In this paper the RBF neural network is applied to predict the cost of the solid rocket motor.

And the intelligent optimizing network can be classified as genetic algorithm network, neural risk element network and risk neural network.

Results The precision of the Bayes and neural network algorithms is better than the decision tree algorithm.

Neural Network, as an important part of AI, is fit for the solution of complex clustering which with mass parameters.

This paper introduces a detection algorithm for the ship fire based on fuzzy inference system, neural network and genetic algorithm.

A new neural network algorithm with fuzzy logic compensation was proposed and applied to an aero- engine rotating speed control system.

A competitive neural network with a combination of BP network, neural network and expert system combining ideas.

Recognizing the smashed-coal-images and block-coal-images was simulated with a probabilistic neural network.

The multi-information influencing the fermentation process is fused by Neural Network to obtain the predicting of the fermentation output.

The emergence of neural network provide effective tools for a system with highly nonlinear input-output relations.

The method of it is to apply rough set and neural network to fault diagnosis of aluminium electrolysis.

A non gradient neural network is presented for solving a kind of the general linear complementarity problem.

The results show that the neural network can provide effectively research for the anti-vibration efficiency of vehicle front suspension.

Conclusion BP artificial neural network can be used to forecast for disease incidence or mortality.

Results of the simulation show that the accuracy is higher than any of traditional Neural Network.

The experimental results show that wavelet neural network is an effective method in signal denoising of ultrasonic detection.

Through taking the database of the test as the example of neural network training samples, we obtain statistical analysis data.

at last the practicality and validity of neural network PID controller is proved by the experiment that the water temperature is controlled.

BP neural network is a very new subject. It consists of computer science, biology, recognition science, physics, mathematic and others.

The corrosion of carbon steels in the oil field soil and its influencing factors were studied with artificial neural network technique.

ADALINE neural network algorithm for the realization of the traditional algorithms for optimization!

The results demonstrated that the proposed method has better explanation and robustness, compared with the BP neural network based method.

BP neural network, as its nature of gradient descent method, is easy to fall into local optimum.

The methods followed are stepwise linear discriminant analysis (linear analysis) and artificial neural network (nonlinear analysis).

Then further study is stretched out of the blade extruder energy consumption , based on the experimental and neural network model .

It has become a hot topic in the application of Artificial Neural Network (ANN) and Genetic Algorithm (GA) to solute concrete structure.

Another is to examine faults of asynchronous Motors in terms of BP neural network based on Particle Swarm Optimization(PSO).

Artificial neural network was taken for a objective and practical method in matter classification.

Neural network can be a useful measure to forecast the property of anticorrosion coating.

For the uncertain characteristics of SPECT images, it is hard to achieve good segmentation result if only the BP neural network is employed.

Methods employed include documentation analysis, the coordinated development degree model analysis and BP neural network model analysis.

Artificial neural network(ANN) and its application inthe anaerobic digestion process are reviewed.

Car used to enhance learning (Q learning), using neural network Q function approximation.

Therefore, 9 reservoir parameters are selected, and neural network system is applied to obtain the evaluation parameters' weighting values.

Also, this paper demonstrates superiority of the IGA-NN by comparing it with the neural network optimized by standard genetic algorithm.

Further more, the application of Neural Network Ensembles improved the network generalization and enhanced the forecasting performance.

Neural network model is integrated with an effective genetic algorithm to find the optimum process parameter values.

in the paper , viscoelastic theory and ann ( artificial neural network ) are applied to estimate ds and draw the curve of deformation.

Meanwhile, a linear regression model and a BP neural network model for predicting the springback quantity were set up.

Secondly, we build up a financial crisis early warning model, which combines Genetic Algorithm and BP Neural Network.

Simulation result shows, that Artificial neural network is a powerful tool for trajectory parameter approach with satisfactory accuracy.

This paper proposes a method which can determine the suitable structure in the hidden layer of a neural network.

Finally use neural network to do algorithm for the main steam pressure system identification.

Here used the PNN neural network to determine the position and RBF neural network identify the section stiffness of steel supports.

Secondly, this dissertation also demonstrates the feasibility of realizing the speed identification through neural network.

Objective To make a research into the application of artificial neural network model in intestinal cancer.

The experiment result of recognition rate is about 98% which has a better performance than distance classification and neural network.

Results show that the developed neural network controller has good performance of velocity tracking, and control efficacy is obvious.

These conclusions were obtained: The whole accuracy of BP neural network model is better than the factor analysis e model.

The characteristics of Rough set theory and Artificial Neural Network are very bright and obviously complementary.

He has found that his evolved brains have become better than traditional neural network brains at the tasks he sets.

A new method for the structural active control may be offered by predicting the structural response with RBF neural network.

Artificial neural network and its application in near infrared spectroscopy are presented in brief.

Experimental results showed that radial neural network method can obtained good classification effect through training of specimen.

Then, these rules are applied to constructing neural cell numbers and relative parameters in fuzzy neural network.

Wavelet recurrent neural network was used to establish the model of long range batch process.

Lastly, we do a research on the application of the neural network model of rolling force to load distribution optimization for cold rolling.

When learning occurs, neuro-chemical communication between neurons is facilitated, in other words a neural network is gradually established.

Numerical Simulation of Compressed Column of Wet Outside Coated Steel and Artificial Neural Network Prediction of Ultimate Bearing Capacity.

The validity of structural damage detection using neural network strongly depends on the training sample.

A neural network method is applied to microwave tomography to reconstruct the image of complex permittivity in this paper.

artificial neural network has excellent performance in adapting , nonlinear mapping etc . , and it is widely used to predict chaotic signal.

The simulation results also indicate that the neural network predictive can effectively solve the problem induced by hysteresis.

It mainly consists of a thick tin oxide gas sensor array and radial basis function(RBF) neural network.

A forward model and an inverse model of a MR damper were established by using BP neural network.

Artificial neural network algorithm for the classic, very conducive to learning artificial neural networks.

LVQ (Learning Vector Quantization) neural network is adopted in gate network and network output is obtained by the nearest rule in space.

The project further studies brain-like information processing mechanism of the deeply-layered neural network.

The results indicate that the prediction performance of neural network is superior to multiple regression equations.

This fuzzy neural network used the B-spline basis function as membership function whose shape can be adjusted on line.

Compared with the ARIMA, regression analysis, gray model, support vector machines and neural network prediction methods.

The exploratory application result shows that the neural network-based system is practical for mining engineering.

The International Neural Network Society promotes the understanding of biological and artificial neural networks.

In this paper, a method was provided for computing natural annual runoff with a neural network BP model.

The chaotic sequences are very easily generated by changing weights of neural network model, and their number is large.

This paper presents a method used to the current transformer excitation characteristic modeling based on RBF neural network.

The paper summarizes the application of artificial neural network in groundwater evaluation, and discusses the problems that still exist.

then, the neural network technique is applied to find the damage from those possible locations, and to assess the degree of damage.

The results of example show that it possesses objectivity and practicability to assess environmental quality by using B P neural network.

A probabilistic neural network(PNN) model and its application to fault diagnosis are researched.

The paper establishes RBF neural network to predict water holdup, and the measurement precision can be improved.

This paper establishes dynamic forward feedback correction model with the method of combining Bayes regularization and BP neural network.

Then BP neural network is trained by use of the unified sample groups and used to analog fault diagnosis at sub-network-level.

Artificial neural network technology is applied to water inrush forecast of coal seam floor of Feicheng mine area.

BP neural network and Bayesian network are mature technology used in pattern recognition.

Thus, a modified BP neural network model was established for groundwater environmental vulnerability evaluation.

This paper presents an algorithm combining neural network and evolution strategy to solve the optimization problem in image restoration.

A rough neural network method is proposed to solve the problems in an information system with interval numbers.

Protein abundances from 16 patients were used to train an artificial neural network to create a prediction algorithm.

And neural network has a variety of applications in exploration of geology, gravity, magnetism, electricity, and seismic.

Compared with BP Neural Network, the simulation result of predict shows that the presented method based on SVM is more accurate.

On the basis of the merits of fuzzy theory and factor neural network, a new model for software engineering quality assessment is proposed.

A novel handwritten numeral recognition approach was presented using multi-wavelet neural network clusters to expand contour shell.

This paper presents a new optimized neural network for nonlinear convex programming problems with both quality and inequality constraints.

finally realize blind signal separation: with the fast independent component analysis and neural network methods!

The artificial neural network possesses rather strong non-linear processing ability and approaching capability.

A genetic neural network control method for variable displacement pump of hydraulic dozer with the energy saving purpose is suggested.

BP neural network; logging interpretation; lithology; physical parameters; oil content.

An artificial neural network was utilized to analyze the collected data and estimate the performance of the sensor arrays.

The fault diagnosis system fulfills fault diagnosis and orientation in virtue of artificial neural network and database technique.

The drawbacks in traditional expert system could be overcome by integrating expert system with neural network.

A Boolean property that indicates whether the Microsoft_Neural_Network algorithm is enabled.

then the texture features were picked up and the neural network was applied to detect the defects.

One kind of alternant gradient algorithm for improving the training of Radial Basis Function (RBF) neural network is proposed.

BP neural network was med as classifier in the model and its parameters were optimized ac cording to experiments.

The static neural network is short of dynamic behavior. Dynamic neural network is described by differential equations with feedback loop.

Finally researches the path planning of robot, applies both neural network and immune evolution algorithm to the path planning of robot.

The result of the experiment shows that the method based on SVR method is more performance compared to BP Neural Network.

It was put forward to apply neural network method to process the dynamic compression test data of molded pulp products.

These instantaneous frequencies are taken as the input pattern to train the Neural Network classifier.

By reduction processing to the import space, this method adopts artificial neural network for data mining on the reduced training data.

A model was developed to estimate the traffic pollutant concentrations along roadside in one step with artificial neural network technique.

A three layers neural network of bidirectional associated memory (BAM) with dilation and translator parameters is presented.

A cascaded hybrid wavelet neural network (WNN) adaptive blind equalizer is proposed for severe nonlinear distortion channel.

The BP neural network is trained through the input picture samples, the output temperature samples and the suitable emissive rate.

One five-layer fuzzy neural network model was built for the forecasting COD of papermaking wastewater treatment process.

CONCLUSION Dosage of ciclosporin may be forecasted using artificial neural network.

The visual control system was validated using a serial BP neural network and satisfactory steering control results were obtained.

We integrate the genetic algorithms with BP neural network and design a genetic based backpropagation neural network classifier.

An self-correction PID chaos control scheme consisting of neural network identifier and neural network controller was presented.

Application of Wavelets Neural Network to Temperature Compensation of Diffused-Silicon Pressure Transducer P.

Therefore the neural network models considering stochastic affects should be finer models.

Selected the optimal combination. (4) BP neural network used to predict the strength of concrete strength.

Finally, verify the prediction accuracy of BP neural network model and the validity of the new indicator system through examples.

BP neural network can be used to correct the output date of navigation periscope.

Artificial neural network (ANN); Tumor marker; Colorectal carcinoma; Diagnosis.

Compared with the traditional method, the neural network has higher ability in the mapping of the fault symptom and the fault mode.

This paper presents an online identification method for guided bomb based adaptive wavelet orthogonal basis neural network.

This course discusses the theoretical background and applications of fuzzy control, neural network control and gray theory.

neural network is deeply researched as representation of eager classification . perceptron is selected.

In this thesis a method combining wavelet analysis and artificial neural network (ANN) is presented to forecast Market Clearing Price (MCP).

Global and Local price forecasting model based recurrent neural network is proposed and applied for the New England Market.

Describing the type of fuzzy neural network and training algorithm, neural network and fuzzy theory combined forecasting model.

Finally, we use new samples to test and compare the performance of the original neural network classifier with the one after optimization.

To test the dynamic property, this pneumatic fatigue test system was identified by a recursive BP neural network.

Artificial neural network have the ability of self-organization learning to input pattern, then formed certain self-awareness.

With the help artificial neural network, the appraisal personnel can provide a rapid, accurate and fair real estate appraisal.

The artificial neural network (ANN) method for transient loss modeling in power devices is proposed and verified by experimental data.

Then, a precise searching is realized in the space with the conventional neural network algorithm to finish the blind equalization.

The method of applying artificial BP neural network to physical parameters forecasting in laser working computer simulation is studied.

Neural network technique establishes the high accuracy variable's non-linear reflecting model with less experiment workload.

A grey predicting model was combined with neural network to establish a mixed predicting model for oilfield production prediction.

This project is accomplished by license plate location with wavelet and character recognition with improved BP neural network.

It also provides important theoretical basis for the understanding of neural network ensembles .

The training data is inevitably contaminated with noise. At first the neural network is represented with a probability relation.

At last, combining with texture features, SAR image is classified with the classical maximum likelihood and BP neural network.

The introduction study rate speeds up the BP neural network the training speed.

The simulation results show that the adaptive neural network system presented in this chapter is correct and feasible.

After the implement of the rule-based expert system, there is a further study of the neural network expert system.

This paper discussed the application of artificial neural network at the resistance spot welding of aluminum alloy.

Compensatory fuzzy neural network is a hybrid system which integrates compensation fuzzy logic and neural network.

Then the leakage points were recognized and positioned via weighted minimum distance classifier and neural network.

It is proved that the neural network model is an effective model to describe the nonlinear behavior of pilots.

A new method, called Grayscale Iteration Threshold Pulse Coupled Neural Network (GIT-PCNN), was proposed for image segmentation.

Conclusion BP neural network models provide a new approach to the research of intestinal cancer and has ideal prospects of application.

Finally, images can be classified by extracted feature sets using neural network.

A multistage neural network model for unconstrained handwritten numerals recognition is proposed, which is composed of many stages.

The artificial neural network(ANN) and the fuzzy recognition(FR) are information process systems to simulate biological mechanism.

The using patterns of NN(neural network)in fault diagnosis are researched. The way of NN(neural network)in fault diagnosis are discussed.

Thirdly, adaptive sliding mode control approach is proposed for a class of unknown SISO affine nonlinear system based on neural network.

Actual data were utilized to train BP neural network that can be used to accurately forecast submarine sonar self-noise.