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Nonlinear control of benchmark problems using TSK-type fuzzy neural network

NEURAL COMPUTING & APPLICATIONS 2013年第1-Sup期23卷 S83-S94页

作者： Lee, Ching-Hung Lai, Wei-Yu Natl Chung Hsing Univ Dept Mech Engn Taichung 402 Taiwan

This paper proposes a TSK-type fuzzy neural network system (TFNN) for identifying and controlling nonlinear control benchmark problem system. It is available for nonlinear dynamic system with uncertainties. The TFNN system can construct and learn its knowledge base from the input-output training data firstly. Thus, a nonlinear system can be represented by several if-then rules with Gaussian membership functions and TSK-type consequent parts. Based on the learned TFNN system, a robust fuzzy controller is proposed, which combines linear matrix inequality-based fuzzy controller and fuzzy sliding model controller. Rigorous proof of asymptotic stability for the closed-loop system is presented via Lyapunov stability theorem. Several examples are presented to illustrate the effectiveness of our approach.

关键词： Nonlinear control Fuzzy neural network TSK-type fuzzy systems back-propagation algorithm

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A training algorithm for multilayer neural networks of hard-limiting units with random bias

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IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES 2000年第6期E83A卷 1040-1048页

作者： Zhu, HB Eguchi, K Tabata, T Hiroshima Kokusai Gakuin Univ Hiroshima 7390321 Japan Kumamoto Natl Coll Technol Kumamoto 8611102 Japan

The conventional back-propagation algorithm cannot be applied to networks of units having hard-limiting output functions, because these functions cannot be differentiated. In this paper, a gradient descent algorithm suitable for training multilayer feedforward networks of units having hard-limiting output functions, is presented. In order to get a differentiable output function for a hard-limiting unit, we utilized that if the bias of a unit in such a network is a random variable with smooth distribution function, the probability of the unit's output being in a particular state is a continuously differentiable function of the unit's inputs. Three simulation results are given, which show that the performance of this algorithm is similar to that of the conventional hack-propagation.

关键词： hard-limiting multilayer neural network back-propagation algorithm learning of neural networks sigmoid and threshold functions

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Predicting vapor pressures using neural networks

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ATMOSPHERIC ENVIRONMENT 1997年第5期31卷 741-753页

作者： Potukuchi, S Wexler, AS UNIV DELAWARE DEPT MECH ENGNNEWARKDE 19716

Calculating surface vapor pressures of volatile inorganic components, nitric acid, hydrochloric acid and ammonia, is essential for modeling condensation and evaporation processes occurring in atmospheric aerosols. The vapor pressure of these compounds depends on temperature, relative humidity, phase state, and particle composition, and their calculation consumes an enormous amount of computer time in Eulerian photochemical/aerosol models. Here we use a thermodynamic model to generate a large set of vapor pressure data as a function of aerosol composition, relative humidity, and temperature. These data are then used as a training set for neural networks. Once the networks memorize the data, interpolation of vapor pressures for intermediate compositions, temperatures and relative humidities is automatic. The neural network models are able to reproduce the values predicted by the thermodynamic models accurately and are 4-1200 times faster depending on atmospheric conditions and the assumptions employed in the thermodynamic calculations. Copyright (C) 1996 Elsevier Science Ltd.

关键词： vapor pressures atmospheric aerosols thermodynamic equilibrium neural networks back-propagation algorithm atmospheric modeling

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Neural learning for articulatory speech synthesis under different statistical characteristics of acoustic input patterns

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COMPUTERS & ELECTRICAL ENGINEERING 2003年第6期29卷 687-702页

作者： Altun, H Curtis, KM Yalcinoz, T Nigde Univ Dept Elect & Elect Engn TR-51100 Nigde Turkey Univ W Indies Dept Math & Comp Sci Kingston 7 Jamaica

Input data representation is highly decisive in neural learning in terms of convergence. In this paper, within an analytical and statistical framework, the effect of the distribution characteristics of the input pattern vectors on the performance of the back-propagation (BP) algorithm is established for a function approximation problem, where parameters of an articulatory speech synthesizer are estimated from acoustic input data. The aim is to determine the optimum statistical characteristics of the acoustic input patterns in order to improve neural learning. Improvement is obtained through a modification of the statistical characteristics of the input data, which reduces effectively the occurrence of node saturation in the hidden layer. (C) 2002 Elsevier Science Ltd. All rights reserved.

关键词： back-propagation algorithm statistical dependency of neural learning articulatory speech synthesizer

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Tool wear monitoring using artificial neural network based on extended Kalman filter weight updation with transformed input patterns

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JOURNAL OF INTELLIGENT MANUFACTURING 2010年第6期21卷 717-730页

作者： Purushothaman, Srinivasan Multimedia Univ Fac Engn & Technol Melaka Malaysia

The condition of the tool in a turning operation is monitored by using artificial neural network (ANN). The recursive Kalman filter algorithm is used for weight updation of the ANN. To monitor the status of the tool, tool wear patterns are collected. The patterns are transformed from n-dimensional feature space to a lower dimensional space (two dimensions). This is done by using two discriminant vectors phi(1) and phi(2). These discriminant vectors are found by optimal discriminant plane method. Thirty patterns are used for training the ANN. A comparison between the classification performances of the ANN trained without reducing the dimensions of the input patterns and with reduced dimensions of the input patterns is done. The ANN trained with transformed tool wear patterns gives better results in terms of improved classification performance in less iteration, when compared with the results of the ANN trained without transforming the dimensions of the input patterns to a lower dimension.

关键词： back-propagation algorithm Extended Kalman filter Optimal discriminant plane n-Dimensional vector 2 Dimensional vector Transformation

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Mapping of neural network models onto massively parallel hierarchical computer systems

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JOURNAL OF SYSTEMS ARCHITECTURE 1999年第11期45卷 919-929页

作者： Mahapatra, S Mahapatra, RN Chatterji, BN Indian Inst Technol Dept Elect & Elect Commun Engn Kharagpur 721302 W Bengal India

This paper investigates the proposed implementation of neural networks on massively parallel hierarchical computer systems with hypernet topology. The proposed mapping scheme takes advantage of the inherent structure of hypernets to process multiple copies of the neural network in the different subnets, each executing a portion of the training set, and finally combines the weight changes in the subnets to adjust the synaptic weights in all the copies. An expression is derived to estimate the time for all-to-all broadcasting, the principal mode of communication in the parallel implemention of neural networks. This is later used to estimate the time required for executing various execution phases in the neural network algorithm, and thus, to estimate the speedup performance of the proposed implementation. (C) 1999 Published by Elsevier Science B.V. All rights reserved.

关键词： multilayer perceptron training set parallelism hypernet subnets back-propagation algorithm speedup performance

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Modeling slump of ready mix concrete using genetic algorithms assisted training of Artificial Neural Networks

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EXPERT SYSTEMS WITH APPLICATIONS 2015年第2期42卷 885-893页

作者： Chandwani, Vinay Agrawal, Vinay Nagar, Ravindra Malaviya Natl Inst Technol Jaipur Dept Civil Engn Jaipur Rajasthan India

The paper explores the usefulness of hybridizing two distinct nature inspired computational intelligence techniques viz., Artificial Neural Networks (ANN) and Genetic algorithms (GA) for modeling slump of Ready Mix Concrete (RMC) based on its design mix constituents viz., cement, fly ash, sand, coarse aggregates, admixture and water-binder ratio. The methodology utilizes the universal function approximation ability of ANN for imbibing the subtle relationships between the input and output variables and the stochastic search ability of GA for evolving the initial optimal weights and biases of the ANN to minimize the probability of neural network getting trapped at local minima and slowly converging to global optimum. The performance of hybrid model (ANN-GA) was compared with commonly used back-propagation neural network (BPNN) using six different statistical parameters. The study showed that by hybridizing ANN with GA, the convergence speed of ANN and its accuracy of prediction can be improved. The trained hybrid model can be used for predicting slump of concrete for a given concrete design mix in quick time without performing multiple trials with different design mix proportions. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Artificial Neural Networks Genetic algorithms back-propagation algorithm Lavenberg Marquardt training algorithm Concrete slump Ready mix concrete

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Controller Application of a Multi-Layer Quantum Neural Network with Qubit Neurons

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JOURNAL OF ADVANCED MECHANICAL DESIGN SYSTEMS AND MANUFACTURING 2012年第4期6卷 526-540页

作者： Takahashi, Kazuhiko Kurokawa, Motoki Hashimoto, Masafumi Doshisha Univ Dept Informat Syst Design Kyotanabe Kyoto 6100321 Japan Doshisha Univ Grad Sch Kyotanabe Kyoto 6100321 Japan Doshisha Univ Dept Intelligent Informat Engn & Sci Kyotanabe Kyoto 6100321 Japan

This paper investigates a quantum neural network and discusses its application in control systems. A learning-type neural network-based controller that uses a multi-layer quantum neural network having qubit neurons as its information processing unit is proposed. Three learning algorithms;a back-propagation algorithm, a conjugate gradient algorithm and a real-coded genetic algorithm, are investigated to supervise the training of the multi-layer quantum neural network. To evaluate the learning performance and the capability of the quantum neural network-based controller, we conducted computational experiments for controlling a nonlinear discrete-time plant and a nonholonomic system - in this study a two-wheeled robot. The results of computational experiments confirm both the feasibility and the effectiveness of the quantum neural network-based controller and that the real-coded genetic algorithm is suitable for the learning method of the quantum neural network-based controller.

关键词： Quantum Neural Network Qubit Neuron back-propagation algorithm Conjugate Gradient algorithm Real-Coded Genetic algorithm Direct Controller Nonholonomic Robot

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Artificial neural networks to support petrographic classification of carbonate-siliciclastic rocks using well logs and textural information

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JOURNAL OF APPLIED GEOPHYSICS 2015年 117卷 118-125页

作者： Silva, Adrielle A. Lima Neto, Irineu A. Missagia, Roseane M. Ceia, Marco A. Carrasquilla, Abel G. Archilha, Nathaly L. North Fluminense State Univ LENEP UENF BR-27925310 Macae RJ Brazil

Petrographic class identification is of great importance to petroleum reservoir characterization and wellbore economic viability analysis, and is usually performed using core or geophysical log analysis. The coring process is costly, and well log analysis requires highly specific knowledge. Thus, great interest has arisen in new methods for predicting the lithological and textural properties of a wide area from a small number of samples. The artificial neural network (ANN) is a computational method based on human brain function and is efficient in recognizing previously trained patterns. This paper demonstrates petrographic classification of carbonate-siliciclastic rocks using a back-propagation neural network algorithm supported by elastic, mineralogical, and textural information from a well data set located in the South Provence Basin, in the southwest of France. The accuracy of the testing suggests that an ANN application offers an auxiliary tool for petrographic classification based on well data, specifically for prediction intervals in wells that have not been sampled or wells adjacent to sampled wells. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Artificial neural network back-propagation algorithm Recognizing patterns Petrographic classification Carbonate-siliciclastic rocks

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Neural network for wave forecasting among multi-stations

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OCEAN ENGINEERING 2002年第13期29卷 1683-1695页

作者： Tsai, CP Lin, C Shen, JN Natl Chung Hsing Univ Dept Civil Engn Taichung 402 Taiwan

Unlike in the open sea, the use of wind information for forecasting waves may encounter more ambiguous uncertainties in the coastal or harbor area due to the influence of complicated geometric configurations. Thus this paper attempts to forecast the waves based on learning the characteristics of observed waves, rather than the use of the wind information. This is reported in this paper by the application of the artificial neural network (ANN), in which the back-propagation algorithm is employed in the learning process for obtaining the desired results. This model evaluated the interconnection weights among multi-stations based on the previous short-term data, from which a time series of waves at a station can be generated for forecasting or data supplement based on using the neighbor stations data. Field data are used for testing the applicability of the ANN model. The results show that the ANN model performs well for both wave forecasting and data supplement when using a short-term observed wave data. (C) 2002 Elsevier Science Ltd. All rights reserved.

关键词： neural network back-propagation algorithm wave forecasting data supplement

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