ON THE CAPACITY OF MULTILAYER NEURAL NETWORKS TRAINED WITH BACKPROPAGATION

2000 ◽  
Vol 10 (04) ◽  
pp. 281-285
Author(s):  
E. N. MIRANDA
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Learning Ability ◽  
Sharp Change ◽  
Maximum Capacity ◽  
Multilayer Neural Networks ◽  
Hidden Neurons

The capacity of a layered neural network for learning hetero-associations is studied numerically as a function of the number M of hidden neurons. We find that there is a sharp change in the learning ability of the network as the number of hetero-associations increases. This fact allows us to define a maximum capacity C for a given architecture. It is found that C grows logarithmically with M.

Effects of Nonlinear Synapses on the Performance of Multilayer Neural Networks

1996 ◽  
Vol 8 (5) ◽  
pp. 939-949 ◽  
Author(s):  
G. Dündar ◽  
F-C. Hsu ◽  
K. Rose
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Multilayer Neural Networks ◽  
Circuit Simulations ◽  
Multiplier Circuit

The problems arising from the use of nonlinear multipliers in multilayer neural network synapse structures are discussed. The errors arising from the neglect of nonlinearities are shown and the effect of training in eliminating these errors is discussed. A method for predicting the final errors resulting from nonlinearities is described. Our approximate results are compared with the results from circuit simulations of an actual multiplier circuit.

UNSUPERVISED COMPETITIVE LEARNING IN NEURAL NETWORKS

1989 ◽  
Vol 01 (02) ◽  
pp. 177-186
Author(s):  
Atilla E. Gunhan ◽  
László P. Csernai ◽  
Jørgen Randrup
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Unsupervised Learning ◽  
Lateral Inhibition ◽  
Competitive Learning ◽  
Learning Ability ◽  
Neural Systems ◽  
Training Set

We study an idealized neural network that may approximate certain neurophysiological features of natural neural systems. The network contains a mutual lateral inhibition and is subjected to unsupervised learning by means of a Hebb-type learning principle. Its learning ability is analysed as a function of the strength of lateral inhibition and the training set.

THE UNIQUENESS THEOREM FOR COMPLEX-VALUED NEURAL NETWORKS WITH THRESHOLD PARAMETERS AND THE REDUNDANCY OF THE PARAMETERS

2008 ◽  
Vol 18 (02) ◽  
pp. 123-134 ◽  
Author(s):  
TOHRU NITTA
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Finite Group ◽  
Uniqueness Theorem ◽  
A Finite Group ◽  
Hidden Neurons ◽  
Complex Valued ◽  
Layered Complex ◽  
The Uniqueness Theorem

This paper will prove the uniqueness theorem for 3-layered complex-valued neural networks where the threshold parameters of the hidden neurons can take non-zeros. That is, if a 3-layered complex-valued neural network is irreducible, the 3-layered complex-valued neural network that approximates a given complex-valued function is uniquely determined up to a finite group on the transformations of the learnable parameters of the complex-valued neural network.

scholarly journals Image Classification: A Survey

2020 ◽  
Vol 1 (2) ◽  
pp. 1-9
Author(s):  
Ankita Singh ◽  
◽  
Pawan Singh
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Language Processing ◽  
Classification Accuracy ◽  
Deep Neural Network ◽  
Learning Ability ◽  
Final Decision

The Classification of images is a paramount topic in artificial vision systems which have drawn a notable amount of interest over the past years. This field aims to classify an image, which is an input, based on its visual content. Currently, most people relied on hand-crafted features to describe an image in a particular way. Then, using classifiers that are learnable, such as random forest, and decision tree was applied to the extract features to come to a final decision. The problem arises when large numbers of photos are concerned. It becomes a too difficult problem to find features from them. This is one of the reasons that the deep neural network model has been introduced. Owing to the existence of Deep learning, it can become feasible to represent the hierarchical nature of features using a various number of layers and corresponding weight with them. The existing image classification methods have been gradually applied in real-world problems, but then there are various problems in its application processes, such as unsatisfactory effect and extremely low classification accuracy or then and weak adaptive ability. Models using deep learning concepts have robust learning ability, which combines the feature extraction and the process of classification into a whole which then completes an image classification task, which can improve the image classification accuracy effectively. Convolutional Neural Networks are a powerful deep neural network technique. These networks preserve the spatial structure of a problem and were built for object recognition tasks such as classifying an image into respective classes. Neural networks are much known because people are getting a state-of-the-art outcome on complex computer vision and natural language processing tasks. Convolutional neural networks have been extensively used.

A neural network architecture for data classification

2001 ◽  
Vol 11 (01) ◽  
pp. 33-42 ◽  
Author(s):  
Olivier Lezoray ◽  
Hubert Cardot
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Architecture ◽  
Neural Network Architecture ◽  
Classification Rate ◽  
Improved Performance ◽  
Global Classification ◽  
Hidden Neurons ◽  
Number Of Classes

This article aims at showing an architecture of neural networks designed for the classification of data distributed among a high number of classes. A significant gain in the global classification rate can be obtained by using our architecture. This latter is based on a set of several little neural networks, each one discriminating only two classes. The specialization of each neural network simplifies their structure and improves the classification. Moreover, the learning step automatically determines the number of hidden neurons. The discussion is illustrated by tests on databases from the UCI machine learning database repository. The experimental results show that this architecture can achieve a faster learning, simpler neural networks and an improved performance in classification.

scholarly journals SOC and SOH Monitoring Algorithms for Lithium Batteries Using Multilayer Neural Networks

10.29007/m89x ◽  
2020 ◽  
Author(s):  
Jong Hyun Lee ◽  
Hyun Sil Kim ◽  
In Soo Lee
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Monitoring System ◽  
Lithium Batteries ◽  
Lithium Battery ◽  
Discharge Voltage ◽  
State Of Charge ◽  
State Of Health ◽  
Soc Estimation ◽  
Multilayer Neural Networks

This paper presents a battery monitoring system using a multilayer neural network (MNN) for state of charge (SOC) estimation and state of health (SOH) diagnosis. In this system, the MNN utilizes experimental discharge voltage data from lithium battery operation to estimate SOH and uses present and previous voltages for SOC estimation. From experimental results, we know that the proposed battery monitoring system performs SOC estimation and SOH diagnosis well.

scholarly journals An Overview of Data Mining with Neural Network

2020 ◽  
Vol 7 ◽  
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Neural Networks ◽  
Pattern Recognition ◽  
Artificial Neural Networks ◽  
Learning Ability ◽  
Management Systems ◽  
Important Data ◽  
The Neural Network ◽  
Base Management

With increasing data base management systems applications, large amounts of important data are available much of its knowledge is preserved and concealed. The methods used to extract data from is Data Mining. Various tools are available to forecast the trends that will support decision of people. Neural Networks or Artificial Neural Networks (ANN) have been a promising system in many applications due to their learning ability from data and generalization ability. Neural Networks are used for prediction, classification, forecasting, and pattern recognition. This paper provides a brief overview of Data Mining with the Neural Network, its tools and process

scholarly journals Use of Aposteriori Information in the Implementation of Radar Recognition Systems Using Neural Network Technologies

2019 ◽  
Vol 22 (5) ◽  
pp. 52-60
Author(s):  
Dmitrii F. Beskostyi ◽  
Sergei G. Borovikov ◽  
Yurii V. Yastrebov ◽  
Ilya A. Sozontov
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Synthesis Method ◽  
Learning Ability ◽  
Network Training ◽  
Recognition Systems ◽  
Situational Assessment ◽  
Network Technologies ◽  
Technical Solutions ◽  
Neural Network Technologies

Introduction. The current need to obtain relevant, complete and reliable information about airborne objects has led to the continuous improvement of modern radar recognition systems (MRRS) as part of control systems. The development of modern MRRS has created objective prerequisites for the use of progressive and new methods and algorithms for the processing of signals using neural networks. The use of artificial neural networks with learning ability permits expansion to include many signs of recognition by using information obtained in the process of monitoring airspace.Aim. To formulate the problem and develop proposals for the use of posterior information for airspace control in radar recognition systems using neural network technologies.Materials and methods. Based on an analysis of the structure of a unified information network, an approach was formulated to facilitate the development of MRRS based on training technologies. Using the synthesis method, examples of technical solutions were proposed, which will allow the use of modern methods and signal processing algorithms using a posteriori information generated by the control system.Results. The study identified the principles of neural network training in solving the recognition problem in the process of functioning of radio electronic equipment (REE). The technical solutions pro-posed take the functioning of the integrated radar system into account, allowing the information parameters required for training MRRS in a single information field to be obtained. It is shown that the removal of restrictions associated with the functional autonomy of REE, allows the use of posterior information in the implementation of radar recognition systems. This also allows for an increase in the number of recognition signs used in the algorithms and for the database of portraits to be replenished. Conclusion. MRRS can be developed via training by removing the restrictions associated with the autonomous functioning of RES. This allows for the situational assessment to be enhanced and management decisions to be optimised.

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