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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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NEURAL NETWORKS WITH INTERVAL WEIGHTS FOR NONLINEAR MAPPINGS OF INTERVAL VECTORS

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IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 1994年第4期E77D卷 409-417页

作者： KWON, K ISHIBUCHI, H TANAKA, H Univ of Osaka Prefecture Sakai-shi Japan

This paper proposes an approach for approximately realizing nonlinear mappings of interval vectors by interval neural networks. Interval neural networks in this paper are characterized by interval weights and interval biases. This means that the weights and biases are given by intervals instead of real numbers. First, an architecture of interval neural networks is proposed for dealing with interval input vectors. Interval neural networks with the proposed architecture map interval input vectors to interval output vectors by interval arithmetic. Some characteristic features of the nonlinear mappings realized by the interval neural networks are described. Next, a learning algorithm is derived. In the derived learning algorithm, training data are the pairs of interval input vectors and interval target vectors. Last, using a numerical example, the proposed approach is illustrated and compared with other approaches based on the standard back-propagation neural networks with real number weights.

关键词： INTERVAL ARITHMETIC NONLINEAR MAPPING OF INTERVAL VECTORS FEEDFORWARD NEURAL NETWORKS backpropagation algorithm

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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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Prediction of mean radiant temperature distribution around a building in hot summer days using optimized multilayer neural network model

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SUSTAINABLE CITIES AND SOCIETY 2022年 84卷

作者： Xie, Yuquan Ishida, Yasuyuki Hu, Jialong Mochida, Akashi Hunan Univ Sch Architecture & Planning Changsha Peoples R China Tohoku Univ Grad Sch Engn Dept Architecture & Bldg Sci Sendai Japan Inst Technol Tokyu Construct Sendai Japan Tohoku Univ Postal address: 6-6-11-1201 Aoba Aramaki Aza Aob Sendai 9808579 Japan

Urban warming has become increasingly serious in recent years. Especially in the case of hot summer days (with maximum daily temperature greater than 30C), the number of people suffering from heat stroke increases every year. To mitigate urban warming and adapt to it, many researchers have focused on improving outdoor thermal comfort. The mean radiant temperature (MRT) is one of the most important variables affecting human thermal comfort in outdoor urban spaces;however, the process of MRT calculation requires a lot of computing power and time. The main objective of this study is the optimization of the back propagation and genetic algorithms on the basis of a multilayer neural network (MLNN), as an alternative to the time-consuming and computationally intensive process for quick MRT prediction. In this research work, a neural network was trained using the relevant weather-related and building morphology-related parameters that may affect the MRT from 2014-2018 as training parameters, the distributions of MRT around buildings in hot summer days of 2019 were predicted using an optimized neural network model. The results show that the mean absolute percentage error (MAPE) and root-mean-square error (RMSE) of the optimized model were lower than 1% and 1C.

关键词： backpropagation algorithm Multilayer neural network Mean radiant temperature Outdoor thermal environment Genetic algorithm

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Intelligent Recognition Method of Insufficient Fluid Supply of Oil Well Based on Convolutional Neural Network

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Open Journal of Yangtze Oil and Gas 2021年第3期6卷 116-128页

作者： Yanfeng He Zhenlong Wang Bin Liu Xiang Wang Bingchao Li China University of Petroleum Beijing China Changzhou University Jiangsu China Oilfield Exploration and Development Management Shandong China China University of Petroleum Qingdao China

Traditional methods for judging the degree of insufficient fluid supply in oil wells have low efficiency and limited accuracy. To address this problem, a method for intelligently identifying the degree of insufficient fluid supply in oil wells based on convolutional neural networks is proposed in this paper. Firstly, 5000 indicator diagrams with insufficient liquid supply were collected from the oilfield site, and a sample set was established after preprocessing;then based on the AlexNet model, combined with the characteristics of the indicator diagram, a convolutional neural network model including 4 layers of convolutional layers, 3 layers of down-pooling layers and 2 layers of fully connected layers is established. The backpropagation, ReLu activation function and dropout regularization method are used to complete the training of the convolutional neural network;finally, the performance of the convolutional neural network under different iteration times and network structure is compared, and the super parameter optimization of the model is completed. It has laid a good foundation for realizing the self-adaptive and intelligent matching of oil well production parameters and formation fluid supply conditions. It has certain application prospects. The results show that the accuracy of training and verification of the method exceeds 98%, which can meet the actual application requirements on site.

关键词： Degree of Insufficient Fluid Supply in Oil Wells Indicator Diagram Convolutional Neural Network Alexnet backpropagation algorithm ReLu Activation Function Dropout Regularization

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A Neural - Sliding Mode Controller

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IFAC Proceedings Volumes 1995年第24期28卷 371-376页

作者： Rodica Mihaela Cistelecan Technical University of Cluj-Napoca Dept. of Automation 15 C Daicoviciu St. 3400 CLUJ-NAPOCA ROMANIA

The paper proposes an adaptive version for sliding mode control system, to reduce the chatter without losing the robustness. An extension of the conventional sliding mode control to a new concept, called the neural - sliding mode control, is presented. The control law parameters are adapted continuously, by an artificial neural network, to estimate the optimal sliding mode control function. The artificial neural network is trained by the backpropagation learning algorithm to extract the necessary control information from the plant. Conclusion is that the two control theories - neural control and sliding mode control - complete each other. The theoretical assertions are sustained by numerical simulation results.

关键词： nonlinear systems state space neural control sliding mode control neural networks backpropagation algorithm

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