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International Joint Conference on Neural Networks

作者： Fernando Gaxiola Patricia Melin Fevrier Valdez Oscar Castillo Tijuana Institute of Technology Tijuana México

ISBN: (纸本)9781467361279

In this paper a neural network learning method with lower and upper type-2 fuzzy weight adjustment is proposed. The general mathematical analysis of the proposed learning method architecture and the adaptation of the interval type-2 fuzzy weights are presented. The proposed method is based on research of recent methods that manage weight adaptation and especially type-2 fuzzy weights. In this paper the neural network architecture managing lower and upper type-2 fuzzy weights and the obtained lower and upper final results are presented. The proposed approach is applied to a case of Mackey-Glass time series prediction.

关键词： Neural Networks Type-2 Fuzzy Weights backpropagation algorithm Type-2 fuzzy system Neural network backpropagation algorithms Learning methods Fuzzy back propagation

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A Low Power and Area Efficient CMOS Implementation of Multilayer Feedforward Artificial Neural Network

A Low Power and Area Efficient CMOS Implementation of Multil...

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作者： Patki, Mayuresh Premanand PennState University Libraries

学位级别：Master of Science

With several advancements in medical science being carried out over the past few decades, there has been a constant need to process information artificially, the way it is processed inside the human body. This inherent attribute of Artificial Intelligence (AI) is achieved in practice using Artificial Neural Networks (ANNs). ANNs have been around since 1943 and used since then for artificial information processing and neural *** thesis focuses on the hardware implementation of an artificial neural network using CMOS technology. The design is carried out in the analog domain to exploit certain advantages of analog integrated circuit design, such as, high efficiency, in terms of area and power, and ease of computation. The neural architecture designed is a multilayer feedforward neural network to solve the XOR classification problem, which serves as a benchmark for several complex classification problems that are not linearly separable. Each component circuit of the network, such as the synapse circuit that performs the multiplication operation and the non-linear activation function circuit that acts as squashing function, is designed using MOSFETs operating in the sub-threshold (weak inversion) *** schematic designs are carried out using Cadence OrCAD Capture version 16.6 EDA software and simulated using PSPICE version 16.6, an in-built simulation tool within OrCAD capture. The layout of the individual components and the overall schematic is also done using Electric VLSI Design software version 9.06 on a 200 nm design scale. A consistency check is carried out to ensure equivalency of layout with the schematic, for a potential scope towards chip fabrication using Metal Oxide Semiconductor Implementation Service (MOSIS) foundry. The layout of the proposed neural architecture is found to occupy an area of 0.065 〖mm〗^2, indicating design compactness to a moderate level.

关键词： Artificial Intelligence Artificial Neural Networks Metal Oxide Semiconductor Implementation Service Integrated Circuit CMOS Very Large Scale Integration McCulloch and Pitts neuron Perceptron backpropagation algorithm Synapses Gilbert Multiplier Cell Activation Function Circuit Floating Gate Single Transistor Learning Synapse Post-Synaptic Current Spike Timing Dependent Plasticity Long Term Potentiation Long Term Depression Static Random Access Memory Memristor Mean Square Error Cadence OrCAD Capture Cadence PSpice A/D Electric VLSI Design System Network Consistency Check Layout Vs Schematic Check XOR Classification Problem MATLAB Time Domain Instantaneous Power Dissipation Loading Learning Rate Mixed Signal System on Chip Field Programmable Gate Arrays

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New pruning algorithm for Recurrent Neural Networks

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IFAC Proceedings Volumes 1999年第2期32卷 5225-5230页

作者： Shinji Tomiyama Takayuki Sasaki Itsuo Hatono Hiroyuki Tamura Department of Systems and Human Science Graduate School of Engineering Science Osaka University 1-3 Machikaneyama Toyonaka Osaka 560-8531 JAPAN FAX: +81-6-6850-6341

It is a very important problem how to decide the structure and size of an artificial neural network because the computational quantity of training and the network performance depend on them. In previous studies, there have been several methods proposed to solve the problem but almost all of them are empirical. In this paper, a new pruning algorithm for a RNN (Recurrent Neural Network) based on a theoretical method of linear model reduction is proposed- In the proposed method, the theoretical model reduction method is applied to the linear subsystem composed of units behaving like linear systems in the RNN

关键词： backpropagation algorithm Model reduction Neural networks Nonlinear models Training

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Off-line Handwritten Character Recognition Using Features Extracted from Binarization Technique

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AASRI Procedia 2013年 4卷 306-312页

作者： Amit Choudhary Rahul Rishi Savita Ahlawat Maharaja Surajmal Institute New Delhi India UIET Maharshi Dayanand University Rohtak India Maharaja Surajmal Institute of Technology New Delhi India

The choice of pattern classifier and the technique used to extract the features are the main factors to judge the recognition accuracy and the capability of an Optical Character Recognition (OCR) system. The main focus of this work is to extract features obtained by binarization technique for recognition of handwritten characters of English language. The recognition of handwritten character images have been done by using multi-layered feed forward artificial neural network as a classifier. Some preprocessing techniques such as thinning, foreground and background noise removal, cropping and size normalization etc. are also employed to preprocess the character images before their classification. Very promising results are achieved when binarization features and the multilayer feed forward neural network classifier is used to recognize the off-line cursive handwritten characters.

关键词： OCR Binarization Feature Extraction Character Recognition backpropagation algorithm Neural Network

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Klasifikace vzorů pomocí fuzzy neuronových sítí

Klasifikace vzorů pomocí fuzzy neuronových sítí

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作者： Ollé, Tamás Brno University of Technology

Práce popisuje základy principu funkčnosti neuronů a vytvoření umělých neuronových sítí. Je zde důkladně popsána struktura a funkce neuronů a ukázán nejpoužívan... 详细信息

Práce popisuje základy principu funkčnosti neuronů a vytvoření umělých neuronových sítí. Je zde důkladně popsána struktura a funkce neuronů a ukázán nejpoužívanější algoritmus pro učení neuronů. Základy fuzzy logiky, včetně jejich výhod a nevýhod, jsou rovněž prezentovány. Detailněji je popsán algoritmus zpětného šíření chyb a adaptivní neuro-fuzzy inferenční systém. Tyto techniky poskytují efektivní způsoby učení neuronových sítí.

关键词： neuron umělé neuronové sítě akční potenciál algoritmus zpětného šíření chyb fuzzy logika fuzzy-neuronová síť adaptivní neuro-fuzzy inferenční systém neuron artificial neural networks action potential backpropagation algorithm fuzzy logic fuzzy

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On a weight limit approach for enhancing fault tolerance of feedforward neural networks

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IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 2000年第11期E83D卷 1931-1939页

作者： Kamiura, N Isokawa, T Hata, Y Matsui, N Yamato, K Himeji Inst Technol Dept Comp Engn Himeji Hyogo 6712201 Japan Hyogo Med Univ Dept Econ & Informat Sci Kakogawa 6750101 Japan

To enhance fault tolerance ability of the feedforward neural networks (NNs for short) implemented in hardware, we discuss the learning algorithm that converges without adding extra neurons and a large amount of extra learning time and cycles. Our algorithm modified from the standard backpropagation algorithm (SBPA for short) limits synaptic weights of neurons in range during learning phase. The upper and lower bounds of the weights are calculated according to the average and standard deviation of them. Then our algorithm reupdates any weight beyond the calculated range to the upper or lower bound. Since the above enables us to decrease the standard deviation of the weights, it is useful in enhancing fault tolerance. We apply NNs trained with other algorithms and our one to a character recognition problem. It is shown that our one is superior to other ones in reliability, extra learning time and/or extra learning cycles. Besides we clarify that our algorithm never degrades the generalization ability of NNs although it coerces the weights within the calculated range.

关键词： feedforward neural networks fault tolerance backpropagation algorithm stuck-at faults generalization ability

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Klasifikace vzorů pomocí fuzzy neuronových sítí

Klasifikace vzorů pomocí fuzzy neuronových sítí

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作者： Ollé, Tamás Brno University of Technology

Práce popisuje základy principu funkčnosti neuronů a vytvoření umělých neuronových sítí. Je zde důkladně popsána struktura a funkce neuronů a ukázán nejpoužívan... 详细信息

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Application of an Artificial Neural Network in Pavement Management System

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TEHNICKI VJESNIK-TECHNICAL GAZETTE 2018年 25卷 466-473页

作者： Domitrovic, Josipa Dragovan, Hrvoje Rukavina, Tatjana Dimter, Sanja Univ Zagreb Fac Civil Engn Kaciceva 26 Zagreb 10000 Croatia RDC Doo Trg Lava Mirskog 1-3 Osijek 31000 Croatia JJ Strossmayer Univ Osijek Fac Civil Engn Vladimira Preloga 3 Osijek 31000 Croatia

The era of intensive construction of new roads in Croatia is behind us. Today road agencies are focused on maintaining and preserving existing roads. Selection of an appropriate maintenance strategy is a complex task, which includes factors such as the current condition of the pavement, road classification, traffic volume etc. These factors are usually implemented in pavement management systems. The key components of pavement management systems are pavement performance prediction models such as artificial neural networks. This paper analyses the possibility of using artificial neural networks to evaluate existing pavement condition, and its possible application for defining the maintenance strategy of national roads. A backpropagation neural network was applied on 481.3 km of national roads in the Osijek-Baranja County. The obtained results indicated that artificial neural networks could be used for optimization of maintenance or rehabilitation strategies, and for the assessment of pavement condition at the project and network level.

关键词： artificial neural network backpropagation algorithm pavement maintenance pavement management system

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Battery Lifetime Prediction via Neural Networks with Discharge Capacity and State of Health

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ENERGIES 2022年第22期15卷 8558页

作者： Hemdani, Jamila Degaa, Laid Soltani, Moez Rizoug, Nassim Telmoudi, Achraf Jabeur Chaari, Abdelkader Univ Tunis Dept Elect Engn Natl Higher Engn Sch Tunis ENSIT Lab Ingn Syst Ind & Energies Renouvelables BP 56-1008 Tunis Tunisia Higher Sch Aeronaut Tech & Automobile Construct E Lab Syst & Energies Embarques Transports Parc Univ Laval ChangeRue Georges Charpak F-53000 Laval France

The market share of electric vehicles (EVs) has grown exponentially in recent years to reduce air pollution and greenhouse gas emissions. The principal part of an EV is the energy storage system, which is usually the batteries. Thus, the accurate estimation of the remaining useful life (RUL) of the batteries, for an optimal health management and a decision-making policy, still remains a challenge for automakers. In this paper, the problem of battery RUL prediction is studied from a new perspective. Unlike other estimation strategies existing in the literature, the proposed technique uses an intelligent prediction of the lifespan of lithium-iron-phosphate (LFP) batteries via a modified version of neural networks. It uses a data-driven life estimation approach and optimization method and does not require any prior comprehension and initialization of any parameters of the battery model. To validate and verify the proposed technique, we use LFP battery data sets, and the experimental results showed that the proposed methodology can well learn the characteristic relationship of battery discharge capacities as well as its state of health (SOH), where the battery life cycle changes as the battery ages with time and cycles.

关键词： electric vehicles lithium-iron-phosphate batteries state of health discharge capacity artificial neural networks backpropagation algorithm

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Necessary Conditions for Multilayer Nets having Solutions and Convergent Superiority of Bipolar Nets

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IFAC Proceedings Volumes 1997年第11期30卷 789-794页

作者： Hiromu Gotanda Hiroshi Shiratsuchi Katsuhiro Inoue Kousuke Kumamaru Faculty of Engineering Kinki University in Kyushu Iizuka Fukuoka 820 Japan Faculty of Computer Science and Systems Engineering Kyushu Institute of Technology Iizuka Fukuoka 820 Japan

This paper formulates a necessary condition for multilayer nets to have solutions by a set of normal vectors orthogonal to separation hyperplanes. Comparing the necessary condition to the distributions of normal vectors with the weights and biases initialized ordinarily by random numbers with zero mean, it is derived that bipolar nets are superior to unipolar nets in convergence of the back propagation learning initialized in such an ordinary manner.

关键词： Neural nets backpropagation algorithm convergence nonlinear models

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