neural network

A hybrid Neural Networks have been developed and the simulation techniques have been used to solve complex group scheduling problems.

Although other techniques are coming into the spotlight (noticeably Neural Networks), the minimax tree is at the heart of the best programs.

In the 1990s, improving your neural networks might have seemed as simple as popping a ginkgo supplement and opening up the Sunday crossword.

Scientists are asking whether spirituality can be explained in terms of neural networks, neurotransmitters and brain chemistry.

It might be possible to develop therapeutics that target neural networks for the treatment of inflammatory disorders.

The book has numerous up to date applications in game programming, intelligent agents, neural networks, artificial immune systems, and more.

but as a good optimization method , the researches on using tabu search as the learning algorithms for fuzzy neural networks are very few.

The results have confirmed the validity of neural networks used for this type of application.

The early literatures have shown that the research difficulty in Delayed Cellular Neural Networks is much more than non-delayed one.

The text proposed the temperature compensation with the artificial neural networks to it the software compensation technology.

Therefore, manpower planning is an urgent task. The artificial neural networks can be used to predict.

They identified two distinct neural networks involved in processing the movement transition, located in two separate areas of the brain.

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

The paper is devoted to the exponential stability of a class of neural networks with both discrete time-varying and distributed delays.

It is one of the important methods of pattern recognition to apply neural networks to target classification.

In the other side, since the frost heave is a strongly non-linear problem, the neural networks prediction model is established.

Neural networks are utilized to combine these descriptors in a way to discriminate members of a given fold from members of all other folds.

Secondly, we proposed an adaptive scheme for the synchronization and stabilization of time-delayed neural networks with unknown parameters.

This type making policy with being helpful to a artificial neural networks from object faces the abstract thinking type evolution.

Thus it is proposed that the error grade iterative BP neural networks method be applied to the asphalt mixtures shearing strength forecast.

With continuous development of computer technology, neural networks as a data-processing structure emerge as the times require.

A fuzzy neural networks consisting of two nets was applied to chromosome recognition in this paper.

Then, the Bayesian neural networks were used to predict the plasticity coefficient of strips.

Crudely put, the brain's neural networks can be thought of as electrical circuits.

The adaptive distributed learning rate of neural networks is developed to increase the learning speed and performance greatly.

The predominant feature of the mould is the hybrid approach which combines expert system technologies and neural networks techniques.

Artificial neural networks are combined with the optimum genetic algorithm to resolve the structural parameter recognition problems.

The Project is an optical character recognition application using artificial neural networks.

The logic neurons have received much concern all the time as the important components of neural networks.

The good optimization can be obtained through the integrated mode of neural networks and genetic algorithms.

The techniques of neural networks applied to the prediction of breakout in slab continuous casting are introduced.

New Zealand climate change information derived by multivariate statistical and artificial neural networks approaches .

In this paper, we present and discuss an online gradient method with a penalty term for three-layer BP neural networks.

We consider neural networks as an alternative computational scheme rather than anything else.

The multilayer forward neural networks are used to establish the inverse kinematics models for robot manipulator.

then to determine the number of hidden nodes of RBF neural networks by maximal matrix element method.

Back propagation artificial neural networks (BP-ANN for short) model was applied to evaluate printing color quality.

through the analysis to the substance of running equations of networks , the evolutive theorem of neural networks was proved.

Character recognition use neural networks, and improve some parameters for a higher rate.

Artificial Neural Networks become a hot research with the ability of self-learning, sele-adaptive and self-organization.

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

The emulation value of the neural networks agrees with well, which shows that the proposed modeling method is reasonable.

Unlike neural networks, genetic programming could dynamically evolve mathematical expressions to solve the problem.

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

An intelligent nuclear reactor core controller for load following operations, using recurrent neural networks and fuzzy systems.

Finally, with the help of a learnable artificial neural networks, most of the 3D features of a model could be recognized.

Artificial neural networks were used to construct the model of properties prediction in 7175 aluminium alloy.

A fast approach for automatically generating fuzzy rules from samples using fuzzy neural networks was proposed for 'fuzzy rules explosion'.

At last through the security risk assessment of transformer by neural networks propose some preventive measures.

The almost surely exponential stability of stochastic recurrent neural networks with time-varying delays is investigated.

Soft computing includes Artifical Neural Networks, Fuzzy Logic, Evolutionary Algorithms, Rough Set (RS) Theory, etc.

Artificial Neural Networks and Genetic Algorithms are cutting-edge of complex non-linear science and artificial intelligence.

Two relevant examples are also presented in the paper to illustrate the evolvement characteristics of feed-forward neural networks.

Recently, wavelet neural networks have become a popular tool for non-linear functional approximation.

Firstly, the MATLAB's Neural Networks Toolbox and its common toolbox functions are presented.

After dealing with the neural networks, the characteristics surface reduced error and increased precision.

This paper pursues a fuzzy neural networks approach for fault diagnosis and discusses its models and the choice of the fuzzy operators.

Compared with artificial neural networks, realistic neural networks are much closer to real biological neural networks.

Chapter 3 introduces global exponential stability of a class of fuzzy BAM neural networks with variable delays.

But neural networks - the previous best hope for creating an artificial brain - are software working on standard computing hardware.

A method of bean extruder processing parameter optimization combining neural networks with genetic algorithm was presented.

This can improve the precision and mapping ability of neural networks, which will improve the performance of inner model control algorithm.

This paper described the application of neural networks in predicting mechanical properties of cold-rolled ribbed steel wires and bars.

This paper presents the prediction of Si content of molten iron by adopting methods of multivariate linear regression and neural networks.

The design of fuzzy neural networks consists of structure identification and parameter identification.

Artificial Neural Networks and Support Vector Machine were selected to test classification accuracy and the test results were analyzed.

Gove stated his case that foreign language learning actually makes students smarter by strengthening the brain's neural networks.

I listen to the same mouthful (earful? ) 20 times or more, to help forge the new "neural networks" in my brain.

Artificial intelligence design model for rubber extrusion process was built with BP ANN(Artificial Neural Networks).

In this paper, a mathematical model is developed for an industrial acrylonitrile fluidized bed reactor based on artificial neural networks.

Synapses are inter-neuronal connections that are fundamental working units in neural networks.

The feedforward neural networks has been greatly used in the nonlinear signals processing.

The researchers used a technique called artificial neural networks to look for patterns in the data.

The neural networks and dynamics associated with general reward and social interactions overlap heavily.

An analysis model which can predict grinding force and surface roughness grinding works by using artificial neural networks is introduced.

An optimization algorithm of RBF Neural Networks based on ant colony clustering and a pruning method is proposed.

This paper presents a new framework for artificial neural networks (ANN) based transient stability assessment(TSA).

Acceptable concentration level of mixtures prediction models have been established in the paper originally by using BP neural networks.

First, the paper presents the survey of cybernetic theory. Neural Networks and Fuzzy Control, and the status quo of Wind Turbine Generator.

The global robust exponential stability is investigated for interval neural networks with mixed delay.

Reference and analog-input voltages drive the neural networks, and digital outputs come from the comparators in the networks.

Besides, the divided-line optimizing method is applied to make the neural networks converge more quickly.

Methods Introduce a non-singleton fuzzy system into the structure of the regular neural networks to construct the prediction model.

In addition, the general regression neural networks (GRNN) compensator is used to achieve a good system response.

protein motors, membranes. We also look at collective behavior of biological elements, cellular networks, neural networks, and evolution.

Forecasting Energy prices using neural networks and fuzzy logic and their combination is discussed in .

Their neural networks are cutting edge, boasting reaction times far quicker than human counterparts.

The mathematical models were built by using multiple regression analysis and artificial neural networks and the results were compared.

The research progress and applications of chaotic neural networks (CNN) models in recent years are reviewed.

The BP neural networks model used in fault diagnosis is constructed, and the process of forward reasoning is given.

Using the capabilities of adaptive patten recognition and handling the nonlinear relationships of BP artificial neural networks.

At Google Books, Cadie recommends several books on topics like neural networks and language processing.

Initial Alignment of Inertial Navigation System Using Wavelet Neural Networks.

Use of neural networks to design adaptive noise cancellation system is an important theoretical and practical value.

The group-control system of welding power sources with the combination of fuzzy theory and neural networks was was studied firstly.

Conclusions In this paper, we have given a brief discussion on mobile robot navigation using neural networks.

On the basis of above analysis, the application of the BP neural networks in leak signals identification and classification is made.

Utilizing improved algorithm in BP and FIR neural networks, the update forms of parameters are deduced.

As such, neural networks have been applied to problems such as disease classification and identification of biomarkers.

Supervised learning is the most common technique for training neural networks and decision trees.

Functional network is a recently introduced extension of neural networks.

This research used the kriging method and the AASN neural networks as the tools to construct spatial interpolation model.

In this paper, the soft competition learning algorithms (SCLA) of RBF neural networks are designed.

Finite Element Method substituted with BP Neural Networks to back-analyze parameter of rock and soil.

An adaptive neural networks controller is designed for tilt rotor aircraft platform.

Nowadays most universities have a neural networks group, within their psychology, physics, computer science, or biology departments.

A hierarchical genetic algorithm is proposed to train the parameters and structure of wavelet neural networks with continuous parameters.

Demo of Bard Ermentrout's XPP. Chemical Reaction Networks Versus Neural Networks. Global Stability of Symmetric Networks.

The RF's neural networks link up with the cerebral cortex, which regulates how we think and remember.

Networks 2: Molecular Computing, Self-assembly, Genetic Algorithms, Neural Networks.

This paper applies BP neural networks to the fault diagnosis of turbine supercharging system of diesel engine.

In this paper, we study the stability of fuzzy neural networks with delays and reaction-diffusion terms.

stability of stochastic neutral-type neural networks with time-varying delays.

Rough sets theory is applied to construct neural networks.

Using HVDM-RBF kernel to test and separately compare the prediction result with results got from RBF kernel, LIK kernel and Neural networks.

By using BP artificial neural networks, a method of non-linearity correction for two dimensional PSD device is presented.

This paper investigate the problem of credit risk assessment using an artificial neural networks model.

We also look at collective behavior of biological elements, cellular networks, neural networks, and evolution.

Then, neural networks are applied to predict the aim-beam number.

The selection of neural net structures and training of neural networks are described.

With the full use of computer vision and neural networks, this paper presents an automatic classification method in the stored-grain pests.

Methods: 1. we explain the basic principle of wavelet analysis, artificial neural networks, as well as wavelet neural network. 2.

the workpiece dimension and rough-ness are controlled by fuzzy--neural networks.

Dynamic adjustment of neural network structure and knowledge-increasing learning is a new field of research in neural networks.

The paper proposes an automatic optimization algorithm for neural networks based on active evolution and genetic algorithms.

So the paper presents the corresponding approaches of nondestructive testing for bearing roller cracks based on neural networks.

Classic discrete bipolar neural networks have no abilities to learn knowledge increasingly.

Using GM (1, 1) model and BP artificial neural networks, a short-term and a long-term combination prediction model was established.

The approach based on artificial neural networks and genetic algorithms is effective only if the search space is relatively small.

A heavy rain forecasting technique is developed based on artificial neural networks.

the other is training neural networks, and then uses the trained network model to forecast.

In this paper, a new measurement method of flow velocity in gas-solid two phase flow is presented by using linear neural networks.

For the problem, in this paper, a novel adaptive filter based on fuzzy-neural networks is proposed and applied in signal filtering.

It introduced the physical simulation model based on artificial neural networks on solid rocket engine milling process.

Then adaptive NNs (Neural Networks) are introduced for their structure and approximation ability.

Two new speech detection methods based on neural networks using Barker coded synchronization signal are presented.

My dream was (and still is) to create a framework to implement a new approach the use of neural networks.

A good book on Neural Networks viewed from the physicist perspective.

In the end, the applying future of artificial neural networks on cutting machining was pared. . .

The method of wavelet neural networks is used in fault diagnosis for the aero-engine lubricant system.

Secondly, the discussion on multistability of neural networks is another emphasis in our thesis.

In this paper, a novel edge detection algorithm based on cellular neural networks (CNN) is proposed for nucleated cell detection.

A fast learning hybrid algorithm of feedforward neural networks based on the regularized least squares is presented.

Absrtact: The biologically inspired neural networks based path planning approacbes of mobile robot were introduced.

Based on the principle of neural networks, a decentralized self-tuning control scheme is proposed for time varying systems.

An introduction of the task background and the combination of fuzzy logic and neural networks. 2.

A quality control approach based on neural networks and expert system is presented, and the working principle is described in detail.

Nowdays, Artificial Neural Networks enable us to avoid the complex constitution model to resolve the problem in Geotechnical.

Then, the thesis discusses the forecast model's establishment of BP Neural Networks based on peasants' income in detail.

A novel graphic symbol recognition approach of engineering drawings based on radial basis probabilistic neural networks (RBPNN) is proposed.

Using the neural networks, lateral resistance and drift of the slab are evaluated, and the results indicate that the average error is 5. 4%.

A traffic pattern recognition method of elevator group control systems based on fuzzy neural networks is presented in this paper.

Learning requires the brain to create new neural networks.

Firstly, some typical data mining algorithms to predict are introduced, such as Bayes algorithm, neural networks, clustering algorithm.

So the model of the prediction of grape disease based on adaptive neural networks has the important reference value.

A method of using wavelet transform and neural networks to classify the normal and fatigue sEMG is provided.

Big-data-based analytics today include rule-based systems, statistical analysis and machine-learning techniques such as neural networks.

In chapter three, adaptive control of general nonlinear systems based on neural networks is studied.

A novel algorithm based on auto associative neural networks(AANN)is given for sensor failure diagnosis.

The links below are online references for additional resources in the field of neural networks.

Application of theory on strength reduction FEM was analyzed based on improved BP neural networks.

A new method to recognize the pitch of musical instrument based on intelligent neural networks is presented.

An approach to compensating sensor drift based on recurrent neural networks is discussed in the paper.

This paper presents a new algorithm for edge detection based on homogeneity and time adaptive self-organizing neural networks (TASOM).

new method of nonlinear sensor estimation based on artificial neural networks is proposed.

in the research on fuzzy neural networks , the problem of parameters learning is very important.

In this paper a new method based on neural networks is proposed for aeroengine adaptive real time modeling.

A data fusion algorithm based on adaptive neural networks-fuzzy inference system (ANFIS) and D-S evidence theory is proposed.

Toenhance the PH sensor reliability, the methods based on signal processing and neural networks are discussed.

This approach is superior to the apparently "complex" methods such as Bayesian networks or neural networks.

This paper presents an adaptive coposite control architecture for robot manipulators based on dynamic recurrent neural networks (DRNN).

He has been tweaking his convolutional neural networks ever since.

The first method is the adaptive control by using neural networks based on offline secondary path modeling.

The MSNN model consisted of five parallel multi-layer feed-forward neural networks (MLNN).

In this case, deterministic method can not be achieved, but the artificial neural networks are non-deterministic method can play a role.

He has been tweaking his "convolutional neural networks" (ConvNets) ever since.

A new blind equalization algorithm based on diagonal recurrent neural networks (DRNN) is proposed.

The brain works on neural networks.

We'll look at decision trees (and neural networks) in later essays.

In order to improve performances of fuzzy neural networks (FNN), a method of training FNN using particle swarm optimizers (PSO) is proposed.

Neural networks can be rapidly reorganised in response to new experiences such as going on the web.

Then input a handwritten character, we can recognize it using trained BP Neural networks.

Over the last ten years feed-forward neural networks have become a popular tool for statistical decision making.

A model based on general regression neural networks(GRNN) has been established to predict the end point of batch pulping cooking.

and neural networks, which are computer systems with the ability to learn.

The neural networks have many advantages such as nonlinear map, self-learning, distributional memory, parallel processing and so on.

T-S fuzzy neural network not only has the advantage of fuzzy logic and the neural networks, but also has good learning ability.

One such method is based on the use of Fuzzy Neural Networks.

These are theages when the brain can form the most neural networks.

There are some shortcomings for the conventional image KL transforms based on neural networks.

The RBF neural networks were trained with the data of 10 kinds of pesticides and other gases. The correct recognition rate reaches 83. 3%.

Convergence and Existence of Periodic Solutions for a Class of Discrete- time Model of Two Cellular Neural Networks

The application of artificial neural networks in the surface roughness research by CBN wheels

Prediction of the Surface Roughness in Curve Grinding Based on Evolutionary Neural Networks

Problems of artificial neural networks used for printer color space conversion

Application of Compound Neural Networks in Optical Properties Determination from Backscattering Light of Turbid Media

Exponential stability and existence of periodic solutions for a class of recurrent neural networks with delays

Study of the dynamics of a first-order cellular neural networks with delay

State-of-the-art on application of artificial neural networks in corroded reinforced concrete structural engineering

Asymptotic Behavior of Solutions for a Class of Two Neurons Feed-back Neural Networks model With Two Thresholds

Model, Characteristics and Developmental Direction of Artificial Neural Networks

A Study of a Class of Dynamical Recurrent Neural Networks Based on an Improved Algorithm

Existence of Almost Periodic Solutions for a Class of Shunting Inhibitory Cellular Neural Networks

Study on the Time-series Wind Speed Forecasting of the Wind farm Based on Neural Networks

Globally Asymptotic Stability and Globally Exponential Stability of a Class of Delayed Cellular Neural Networks

Neural Networks Modeling of a Class of Process Control Object and Its Simulation Research

Feed-forward neural networks; Generalization; ability; Gradient descent method; Regularization; Information entropy;

Artificial Neural Networks-Based Study of Vegetations Classification for Aerial Remote Sensing Image

A Study of Forecasting Sintering Characteristics of Ceramic Materials Using BP Artificial Neural Networks

Existence and uniqueness of an almost periodic solution for competitive neural networks with distributed delays and different time-scales

Study of Dual Linear Programming Neural Networks and Selection of Its Learning Rate

Initial Investigation of Cascaded Neural Networks in Hourly Thermal Load Forecast of Next Day for Boiler Plant

Structure Optimization of Radial Basis Probabilistic Neural Networks by the Maximum Absolute Error Combined with the Micro-Genetic Algorithm

The Convergence of Solutions for a Class of Discrete-time Neural Networks Model of Two Neurons

Artificial Neural Networks Identification of Quantitative Coefficients in Refrigeration System Simulation

Study of the Application of Improved BP Neural Networks to Development Cost Prediction in Missle

Mathematical Morphology Degree-operation and its Implementation Using Neural Networks

A Real-time Fault Diagnosis Approach to Liquid Rocket Propulsion Systems Using Mechanism Model and SOM Neural Networks

Existence of Almost Periodic Solutions for Cellular Neural Networks with Distributed Delays and Variable Coefficients

Synchronization of a Class of Neural Networks Containing Hybrid Delays and Sector Nonlinearity

Study of the Fault Diagnosis Method for Diesel Engines Using Two Artificial Neural Networks

Exponential Attraction Domain and Exponential Convergent Rate of Associative Memory Neural Networks with Delays

Adaptive Supervised Distributed Neural Networks and Its Industrial Application

Regulated morphological operation and its implementation using neural networks

Ultrasonic identification of the shapes of tiny pits on an internal surface using BP neural networks

Evaluation of sampling techniques for neural networks applied to computational simulation of structures

Simultaneous Determination of Benzene and Methyl Benzene by Artificial Neural Networks Ultraviolet Spectrophotometry

Existence of Periodic Solutions for Cellular Neural Networks with Distributed Delays and Variable Coefficients

Establishment of Fault Prognosis Model Using Wavelet Neural Networks and Its Engineering Application

Application of neural networks for studying nonlinear relationships between ozone and its precursors

Hybrid nonlinear autoregressive neural networks for permanent-magnet linear synchronous motor identification

Result of global stability for cellular neural networks with time delay

Sufficient and Necessary Condition for Local Exponential Stability of Bidirectional Continuous-Time Associate Memory Neural Networks

Analysis and Prediction of Mechanical Properties of Polyethylene and Its Blends by Artificial Neural Networks

An Improved Algorithm of Fuzzy Neural Networks and Its Application to Vibration Intensity Identification

A Kalman Filter Learning Algorithm for Multilayer Quadratic Neural Networks and Its Convergence Analysis

Convergence of a Class of Bidirectional Associative Memory Neural Networks with Delays

The Universal Approximation Theory and Learning Algorithm of Hierarchical Hyperboloid Function Link Artificial Neural Networks

Research on the Application of BP Neural Networks with a Genetic Algorithms on Forecasting Exchange Rate

Dynamically Weighted Ensemble Neural Networks with Generalized Regression Neural Network for Solving Regression Problems

Case study of rainfall-runoff dynamic model using Elman recurrent artificial neural networks

Studies on Artificial Neural Networks Used in A. C. Oscillographic Chronopotentiometry

The Establishment of BP Neural Networks Model for Financial Crisis Prediction of Listed Companies and Its Application

Neural networks prediction study of trends prediction serial in supply chain management

Neural Networks Improved Flexibility and Its Application to Control Switched Reluctance Motor

A Hierarchic Decomposition Neural Networks Method for Complicated Engineering System Structure Optimization

Function approximation capabilities of intuitionistic fuzzy reasoning neural networks;

Artificial neural networks method for back-calculating pavement structural parameters simultaneously

An Analysis and Improvement of the Shortcomings of the Common Real Number Coding Scheme in Genetic Multilayer Feedforward Neural Networks

State-of-Art of Application of Artificial Neural Networks in Hydrology, Water Resources and Water Environment