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Minimized sodium nitroprusside for mean arterial pressure regulation based on fuzzy rules emulated networks

APPLIED MATHEMATICAL MODELLING 2010年第5期34卷 1292-1310页

作者： Treesatayapun, Chidentree IPN Dept Robot & Adv Mfg CINVESTAV Unidad Saltillo Ramos Arizpe 25900 Mexico N Chiang Mai Univ Dept Elect Engn Chiang Mai 50230 Thailand

An automatic regulation of mean arterial pressure (MAP) through the intravenous infusion of sodium nitroprusside (SNP) based on self adjustable networks called Multi-input fuzzy Rules Emulated Networks (MIFREN) and its proposed learning algorithm is introduced in this article. To optimize the regulation performance and SNP dose, the estimated cost function is obtained by the first self adjustable network used to design the nearly optimum controller. The second network is designed as the direct controller via the human knowledge through the defined IF-THEN rules. The infusion rate of SNP is generated as the control effort to regulate MAP with the possible minimized drug level. All designed parameters such as the learning rates and some constant parameters have been selected to guarantee the system stability and bounded signals by the proof of the proposed theorem. The satisfied performance of the proposed controller is represented by computer simulations and comparison results with other control techniques are demonstrated the lower level of SNP at the steady state. (C) 2009 Elsevier Inc. All rights reserved.

关键词： neuro-fuzzy control neuro-fuzzy learning algorithm Mean arterial pressure regulation neuro-fuzzy Nonlinear discrete-time control system and Identification

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fuzzy rules emulated network and its application on nonlinear control systems

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APPLIED SOFT COMPUTING 2008年第2期8卷 996-1004页

作者： Treesatayapun, C. CINVESTAV Res Ctr Adv Studies Robot & Adv Manufacturing Div Saltillo Coahuila Mexico N Chian Mai Univ Dept Elect Engn Chiang Mai Thailand

In this paper, the discrete-time nonlinear systems identification and control based on an adaptive filter are introduced. This adaptive filter is implemented using the adaptive network called Multi Input fuzzy Rules Emulated Network (MIFREN). Inspired by the neuro-fuzzy network, the structure of MIFREN resembles the human knowledge in the form of fuzzy IF-THEN rules. The initial value of MIFREN's parameters can be easily selected based on the human knowledge. Then the on-line adaptive process is performed to fine tune these parameters, the convergence of the adaptive process is proven by using Lyapunov-theory-based Adaptive Filtering (LAF). In the control system application, MIFREN is applied to control various selected nonlinear systems together with the proposed control law. Computer simulation results indicate that the proposed controller is able to control the target systems satisfactory. (c) 2007 Elsevier B.V. All rights reserved.

关键词： fuzzy control neuro-fuzzy control neuro-fuzzy learning algorithm direct adaptive control

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fuzzy-rule emulated networks, based on reinforcement learning for nonlinear discrete-time controllers

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ISA TRANSACTIONS 2008年第4期47卷 362-373页

作者： Treesatayapun, Chidentree CINVESTAV Saltillo Dept Robot & Manufactory Saltillo 25900 Coahuila Mexico N Chiangmai Univ Dept Elect Engn Chiangmai Thailand

This article introduces an adaptive controller for a class of nonlinear discrete-time systems, based on self adjustable network's called Multi-Input fuzzy Rules Emulated Networks (MIFRENs). and its reinforcement learning algorithm. Because of the Universal function approximation of MIFREN, the first MIFREN called MIFREN, is used to estimate a long-term cost function, which demonstrates its a performance index for the tuning procedure. Another network or MIFREN, is designed as a direct controller via the human knowledge through defined IF-THEN rules. The selection procedure for any system parameters. Such as learning rates and some constant parameters, is represented by the proof of proposed theorems. The System's performance is demonstrated by computer simulations via selected nonlinear discrete-time systems. and corn pari soil results with other controllers to validate theoretical development. (C) 2008 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： neuro-fuzzy control neuro-fuzzy learning algorithm Nonlinear discrete-time

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Adaptive controller with fuzzy rules emulated structure and its applications

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2005年第5期18卷 603-615页

作者： Treesatayapun, C Uatrongjit, S N Chiang Mai Univ Dept Elect Engn Chiang Mai 50230 Thailand Chiang Mai Univ Dept Elect Engn Chiang Mai Thailand

In this paper, the adaptive controller inspired by the neuro-fuzzy controller is proposed. Its structure, called fuzzy rules emulated network (FREN), is derived based on the fuzzy IF-THEN rules. This structure not only emulates the fuzzy control rules but also allows the initial value of controller's parameters to be intuitively chosen. These parameters are further adjusted during system operation using a method similar to the steepest descent technique. The learning rate selection criteria based on Lyapunov's stability condition is also presented. FREN controller is applied to control various nonlinear systems, for examples, the single invert pendulum plant, the water bath temperature control, the high voltage direct current transmission system and the robotic system. Computer simulations results indicate that the proposed controller is able to control the target systems satisfactory. (c) 2005 Elsevier Ltd. All rights reserved.

关键词： fuzzy control neuro-fuzzy control neuro-fuzzy learning algorithm direct adaptive control

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An improvement of neuro-fuzzy learning algorithm for tuning fuzzy rules

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fuzzy SETS AND SYSTEMS 2001年第2期118卷 339-350页

作者： Shi, Y Mizumoto, M Kyushu Tokai Univ Sch Engn Dept Informat Syst Eng Kumamoto 8628652 Japan Osaka Electrocommun Univ Div Informat & Comp Sci Neyagawa Osaka 5728530 Japan

Based on the fuzzy clustering method, we improved a neuro-fuzzy learning algorithm. In this improved approach, before learning fuzzy rules we extract typical data from training data by using fuzzy c-means clustering algorithm, in order to remove redundant data and resolve conflicts in data, and make them as practical training data. By these typical data, fuzzy rules can be tuned by using the neuro-fuzzy learning algorithm. Therefore, the learning time can be expected to be reduced and the fuzzy rules generated by the improved approach are reasonable and suitable for the identified system model, Moreover, the efficiency of the improved method is also shown by identifying nonlinear functions. (C) 2001 Elsevier Science B.V. All rights reserved.

关键词： fuzzy rule generation neuro-fuzzy learning algorithm fuzzy c-means clustering algorithm

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Some considerations on conventional neuro-fuzzy learning algorithms by gradient descent method

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fuzzy SETS AND SYSTEMS 2000年第1期112卷 51-63页

作者： Shi, Y Mizumoto, M Kyushu Tokai Univ Sch Engn Kumamoto 8628652 Japan Osaka Electrocommun Univ Div Informat & Comp Sci Neyagawa Osaka 5728530 Japan

In this paper. we try to analyze several conventional neuro-fuzzy learning algorithms, which are widely used in recent fuzzy applications for tuning fuzzy rules, and give a summarization of their properties in detail. Some of these properties show that the uses of the conventional neuro-fuzzy learning algorithms are difficult or inconvenient sometimes for constructing an optimal fuzzy system model in practical fuzzy applications. (C) 2000 Elsevier Science B.V. All rights reserved.

关键词： fuzzy rule generation neuro-fuzzy learning algorithm Gaussian-type membership function triangular-type membership function product-sum-gravity fuzzy reasoning method simplified fuzzy reasoning method

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