Browsing by Author "Juang, CF"

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A recurrent self-organizing neural fuzzy inference network
(1997)
Juang, CF
;
Lin, CT
;
交大名義發表
;
電控工程研究所
;
National Chiao Tung University
A Recurrent Self-Organizing Neural Fuzzy Inference Network (RSONFIN) is proposed in this paper. The RSONFIN is constructed from a series of dynamic fuzzy rules. The temporal relations embedded in the network are built by adding some feedback connections representing the memory elements to a feedforward neural fuzzy network. Each weight as well as node in the RSONFIN has its own meaning and represents a special element in a fuzzy rule. There are no hidden nodes (i.e., no membership functions and fuzzy rules) initially in the RSONFIN. They are created on-line via concurrent structure identification (the construction of dynamic fuzzy if-then rules) and parameter identification (the tuning of the free parameters of membership functions). The structure learning together with the parameter learning forms a fast learning algorithm for building a small, yet powerful, dynamic neural fuzzy network. Simulations on temporal problems are done finally.
A recurrent self-organizing neural fuzzy inference network
(1999-07-01)
Juang, CF
;
Lin, CT
;
電控工程研究所
;
Institute of Electrical and Control Engineering
A recurrent self-organizing neural fuzzy inference network (RSONFIN) is proposed in this paper, The RSONFIN is inherently a recurrent multilayered connectionist network for realizing the basic elements and functions of dynamic fuzzy inference, and may he considered to be constructed from a series of dynamic fuzzy rules, The temporal relations embedded in the network are built by adding some feedback connections representing the memory elements to a feedforward neural fuzzy network, Each weight as well as node in the RSONFIN has its own meaning and represents a special element in a fuzzy rule. There are no hidden nodes (i.e., no membership functions and fuzzy rules) initially in the RSONFIN, They are-created on-line via concurrent structure identification (the construction of dynamic fuzzy if-then rules) and parameter identification (the tuning of the free parameters of membership functions). The structure learning together with the parameter learning forms a fast learning algorithm for building a small,yet powerful, dynamic neural fuzzy network Two major characteristics-of the RSONFIN can thus be seen:1) the recurrent property of the RSONFIN makes it suitable for dealing with temporal problems and 2) no predetermination, Like the number of hidden nodes, must be given, since the RSONFIN can find its optimal structure and parameters automatically and quickly, Moreover, to reduce the number of fuzzy rules generated, a flexible input partition method,the aligned clustering-based algorithm, is proposed. Various simulations on temporal problems are done and performance comparisons with some existing recurrent networks are also made, Efficiency of the RSONFIN is verified from these results.
An adaptive neural fuzzy filter and its applications
(1997-08-01)
Lin, CT
;
Juang, CF
;
交大名義發表
;
電控工程研究所
;
National Chiao Tung University
A new kind of nonlinear adaptive filter, the adaptive neural fuzzy filter (ANFF), based upon a neural network's learning ability and fuzzy if-then rule structure, is proposed in this paper, The ANFF is inherently a feedforward multilayered connectionist network which can learn by itself according to numerical training data or expert knowledge represented by fuzzy if-then rules, The adaptation here includes the construction of fuzzy if-then rules (structure learning), and the tuning of the free parameters of membership functions (parameter learning), In the structure learning phase, fuzzy rules are found based on the matching of input-output clusters, In the parameter learning phase, a backpropagation-like adaptation algorithm is developed to minimize the output error, There are no hidden nodes (i.e., no membership functions and fuzzy rules) initially, and both the structure learning and parameter learning are performed concurrently as the adaptation proceeds, However, if some linguistic information about the design of the filter is available, such knowledge can be put into the ANFF to form an initial structure with hidden nodes, Two major advantages of the ANFF can thus be seen: 1) a priori knowledge can be incorporated into the ANFF which makes the fusion of numerical data and linguistic information in the filter possible; and 2) no predetermination, like the number of hidden nodes, must be given, since the ANFF can find its optimal structure and parameters automatically. Moreover, in contrast to traditional fuzzy systems where the input-output spaces are partitioned as grid type causing the combinatorial growing of fuzzy rules as the input-output dimensions increase, irregular partitions are done in the ANFF according to the distribution of training data so fewer fuzzy rules will be generated, To demonstrate the performance of the ANFF, two applications, the nonlinear channel equalization and the adaptive noise cancellation, are simulated, Efficiency and advantages of the ANFF are verified by these simulations and comparisons.
An adaptive neural fuzzy filter and its applications
(1996)
Lin, CT
;
Juang, CF
;
電控工程研究所
;
Institute of Electrical and Control Engineering
An adaptive neural fuzzy filter and its applications
(1997-08-01)
Lin, CT
;
Juang, CF
;
電控工程研究所
;
Institute of Electrical and Control Engineering
A new kind of nonlinear adaptive filter, the adaptive neural fuzzy filter (ANFF), based upon a neural network's learning ability and fuzzy if-then rule structure, is proposed in this paper, The ANFF is inherently a feedforward multilayered connectionist network which can learn by itself according to numerical training data or expert knowledge represented by fuzzy if-then rules, The adaptation here includes the construction of fuzzy if-then rules (structure learning), and the tuning of the free parameters of membership functions (parameter learning), In the structure learning phase, fuzzy rules are found based on the matching of input-output clusters, In the parameter learning phase, a backpropagation-like adaptation algorithm is developed to minimize the output error, There are no hidden nodes (i.e., no membership functions and fuzzy rules) initially, and both the structure learning and parameter learning are performed concurrently as the adaptation proceeds, However, if some linguistic information about the design of the filter is available, such knowledge can be put into the ANFF to form an initial structure with hidden nodes, Two major advantages of the ANFF can thus be seen: 1) a priori knowledge can be incorporated into the ANFF which makes the fusion of numerical data and linguistic information in the filter possible; and 2) no predetermination, like the number of hidden nodes, must be given, since the ANFF can find its optimal structure and parameters automatically. Moreover, in contrast to traditional fuzzy systems where the input-output spaces are partitioned as grid type causing the combinatorial growing of fuzzy rules as the input-output dimensions increase, irregular partitions are done in the ANFF according to the distribution of training data so fewer fuzzy rules will be generated, To demonstrate the performance of the ANFF, two applications, the nonlinear channel equalization and the adaptive noise cancellation, are simulated, Efficiency and advantages of the ANFF are verified by these simulations and comparisons.
An on-line self-constructing neural fuzzy inference network and its applications
(1998-02-01)
Juang, CF
;
Lin, CT
;
電控工程研究所
;
Institute of Electrical and Control Engineering
A self constructing neural fuzzy inference network (SONFIN) with on-line learning ability is proposed in this paper, The SONFIN is inherently a modified Takagi-Sugeno-Kang (TSK) type fuzzy rule-based model possessing neural network's learning ability, There are no rules initially in the SONFIN, They are created and adapted as on-line learning proceeds via simultaneous structure and parameter identification, In the structure identification of the precondition part, the input space is partitioned in a flexible way according to a aligned clustering-based algorithm. As to the structure identification of the consequent part, only a singleton value selected by a clustering method is assigned to each rule initially, Afterwards, some additional significant terms (input variables) selected via a projection-based correlation measure for each rule will be added to the consequent part (forming a linear equation of input variables) incrementally as learning proceeds, The combined precondition and consequent structure identification scheme can set up an economic and dynamically growing network, a main feature of the SONFIN, In the parameter identification, the consequent parameters are tuned optimally by either least mean squares (LMS) or recursive least squares (RLS) algorithms and the precondition parameters are tuned by backpropagation algorithm, Both the structure and parameter identification are done simultaneously to form a fast learning scheme, which is another feature of the SONFIN, Furthermore, to enhance the knowledge representation ability of the SONFIN, a linear transformation for each input variable can be incorporated into the network so that much fewer rules are needed or higher accuracy can be achieved, Proper linear transformations are also learned dynamically in the parameter identification phase of the SONFIN, To demonstrate the capability of the proposed SONFIN, simulations in different areas including control, communication, and signal processing are done, Effectiveness of the SONFIN is verified from these simulations.
Application of neural fuzzy network to pulse compression with binary phase code
(2003)
Duh, FB
;
Juang, CF
;
Lin, CT
;
電控工程研究所
;
Institute of Electrical and Control Engineering
To solve the existing dilemma between making good range resolution and maintaining the low average transmitted power, it is necessary for the pulse compression processing to give low range sidelobes in the modern high-resolution radar systems. The traditional pulse compression algorithms based on 13-element Barker code such as direct autocorrelation filter (ACF), least squares (LS) inverse filter, and linear programming (LP) filter have been developed, and the neural network algorithms were issued recently. However, the traditional algorithms cannot achieve the requirement of high signal-to-sidelobe ratio, and the normal neural network such as backpropagation (BP) network usually produces the extra problems of low convergence speed and sensitive to the Doppler frequency shift. To overcome these defects, a new approach using a neural fuzzy network with binary phase code to deal with pulse compression in a radar system is presented in this paper. The 13-element Barker code used as the binary phase signal code is carried out by six-layer self-constructing neural fuzzy network (SONFIN) with supervised learning algorithm. Simulation results show that this neural fuzzy network pulse compression (NFNPC) algorithm has the significant advantages in noise rejection performance, range resolution ability and Doppler tolerance, which are superior to the traditional and BP algorithms, and has faster convergence speed than BP algorithm.
Genetic reinforcement learning through symbiotic evolution for fuzzy controller design
(1998)
Juang, CF
;
Lin, CT
;
電控工程研究所
;
Institute of Electrical and Control Engineering
An efficient genetic reinforcement learning algorithm for designing fuzzy controllers is proposed in this paper. (1) The genetic algorithm (GA) adopted in this paper is based upon symbiotic evolution which, when applied to fuzzy controller design, matches well with the local mapping property of a fuzzy rule. Using this Symbiotic-Evolution-based Fuzzy Controller (SEFC) design method, the number of control trials as well as consumed CPU time are reduced considerably as compared to traditional GA-based fuzzy controller design methods and other types of genetic reinforcement learning schemes. The proposed SEFC design method has been applied to the cart-pole balancing system. Efficiency and superiority of the proposed SEFC have been verified from this problem and from the comparisons with traditional GA-based fuzzy systems.
Genetic reinforcement learning through symbiotic evolution for fuzzy controller design
(2000-04-01)
Juang, CF
;
Lin, JY
;
Lin, CT
;
電控工程研究所
;
Institute of Electrical and Control Engineering
An efficient genetic reinforcement learning algorithm for designing fuzzy controllers is proposed in this paper. The genetic algorithm (GA) adopted in this paper is based upon symbiotic evolution which, when applied to fuzzy controller design, complements the local mapping property of a fuzzy rule. Using this Symbiotic-Evolution-based Fuzzy Controller (SEFC) design method, the number of control trials, as well as consumed CPU time, are considerably reduced when compared to traditional GA-based fuzzy controller design methods and other types of genetic reinforcement learning schemes. Moreover, unlike traditional fuzzy controllers, which partition the input space into a grid, SEFC partitions the input space in a flexible way, thus creating fewer fuzzy rules. In SEFC, different types of fuzzy rules whose consequent parts are singletons, fuzzy sets, or linear equations (TSK-type fuzzy rules) are allowed. Further, the free parameters (e.g., centers and widths of membership functions) and fuzzy rules are all tuned automatically. For the TSK-type fuzzy rule especially, which put the proposed learning algorithm in use, only the significant input variables are selected to participate in the consequent of a rule. The proposed SEFC design method has been applied to different simulated control problems, including the cart-pole balancing system, a magnetic levitation system, and a water bath temperature control system. The proposed SEFC has been verified to be efficient and superior from these control problems, and from comparisons with some traditional GA-based fuzzy systems.
Noisy speech processing by recurrently adaptive fuzzy filters
(2001-02-01)
Juang, CF
;
Lin, CT
;
電控工程研究所
;
Institute of Electrical and Control Engineering
Two noisy speech processing problems-speech enhancement and noisy speech recognition-are dealt with in this paper. The technique we focus on is by using the filtering approach; a novel filter, the recurrently adaptive fuzzy filter (RAFF), is proposed and applied to these two problems. The speech enhancement is based on adaptive noise cancellation with two microphones, where the RAFF is used to eliminate the noise corrupting the desired speech signal in the primary channel, As to the noisy speech recognition, the RAFF is used to filter the noise in the feature domain of speech signals. The RAFF is inherently a recurrent multilayered connectionist network for realizing the basic elements and functions of dynamic fuzzy inference, and may be considered to be constructed from a series of dynamic fuzzy rules. As compared to other existing nonlinear filters, three major advantages of the RAFF are observed: 1) a priori knowledge can be incorporated into the RAFF, which makes the fusion of numerical data and linguistic information possible; 2) owing to the dynamic property of the RAFF, the exact lagged order of the input variables need not be known in advance; 3) no predetermination, like the number of hidden nodes, must be given since the RAFF can find its optimal structure and parameters automatically Several examples on adaptive noise cancellation and noisy speech recognition problems using the RAFF are illustrated to demonstrate the performance of the RAFF.
Temperature control of rapid thermal processing system using adaptive fuzzy network
(1999-04-01)
Lin, CT
;
Juang, CF
;
Huang, JC
;
電控工程研究所
;
Institute of Electrical and Control Engineering
Temperature control of a rapid thermal processing (RTP) system using a proposed self-constructing adaptive fuzzy inference network (SCAFIN) is presented in this paper. First, the physical modeling of a RTP system is done. An integrated model is given for the components that make up a RTP system. These components are the lamp power dynamics, ray-tracing model, and the wafer thermal dynamic model. The models for the components are integrated in a numerical code to give a computer simulation of the complete RTP system. The simulation can be used to investigate the interaction of the furnace, lamp contour, and the control system. Then a direct inverse control scheme using the proposed SCAFIN is adopted to control the temperature of the RTP system. The SCAFIN is inherently a modified TSK-type fuzzy rule-based model possessing neural network's learning ability. There are no rules initially in the SCAFIN. They are created and adapted as on-line learning proceeds via simultaneous structure and parameter identification. Simulation results show that the control approach is able to track a temporally varying temperature trajectory and maintain the uniformity of the spatial temperature distribution of the wafer in the RTP system simultaneously. (C) 1999 Elsevier Science B.V. All rights reserved.
Temperature control with a neural fuzzy inference network
(1999-08-01)
Lin, CT
;
Juang, CF
;
Li, CP
;
電控工程研究所
;
Institute of Electrical and Control Engineering
Although multilayered backpropagation neural networks (BPNN's) have demonstrated high potential in the nonconventional branch of adaptive control, their long training time usually discourages their applications in industry. Moreover, when they are trained on line to adapt to plant variations, the over-tuned phenomenon usually occurs. To overcome the weakness of the BPNN, in this paper we propose a neural fuzzy inference network (NFIN) suitable for adaptive control of practical plant systems in general and for adaptive temperature control of a water bath system in particular. The NFIN is inherently a modified Takagi-Sugeno-Kang (TSK)-type fuzzy rule-based model possessing a neural network's learning ability. In contrast to the general adaptive neural fuzzy networks, where the rules should be decided in advance before parameter learning is performed, there are no rules initially in the NFIN. The rules in the NFIN are created and adapted as on-line learning proceeds via simultaneous structure and parameter identification. The NFIN has been applied to a practical water bath temperature-control system. As compared to the BPNN under the same training procedure, the simulated results show that not only can the NFIN greatly reduce the training time and avoid the over-tuned phenomenon, but the NFIN also has perfect regulation ability. The performance of the NFIN is also compared to that of the traditional PID controller and; fuzzy logic controller (FLC) on the water bath temperature-control system. The three control schemes are compared through experimental studies with respect to set-points regulation, ramp-points tracking, and the influence of unknown impulse noise and large parameter variation in the temperature-control system. It is found that the proposed NFIN control scheme has the best control performance of the three control schemes.
Temperature control with a neural fuzzy inference network
(1996)
Lin, CT
;
Juang, CF
;
Li, CP
;
交大名義發表
;
電控工程研究所
In this paper, we propose a neural fuzzy inference network (NFIN) suitable for adaptive temperature control of a water bath system. The rules in the NFIN are created and adapted as on-line learning proceeds via simultaneous structure and parameter identification. The NFIN has been applied to a practical water bath temperature control system. The performance of the NFIN is compared to that of the PID controller and fuzzy logic controller (FLC) on the water bath temperature control system. The three control schemes are compared through experimental studies. It's found that the proposed NFIN control scheme has best control performance among the three control schemes.
Water bath temperature control with a neural fuzzy inference network
(2000-04-16)
Lin, CT
;
Juang, CF
;
Li, CP
;
電控工程研究所
;
Institute of Electrical and Control Engineering
Although multilayered backpropagation neural networks (BPNN) have demonstrated high potential in the nonconventional branch of adaptive control, its long training time usually discourages their applications in industry. Moreover, when they are trained on-line to adapt to plant variations, the overtuned phenomenon usually occurs. To overcome the weakness of the BPNN, we propose a neural fuzzy inference network (NFIN) in this paper suitable for adaptive control of practical plant systems in general, and for adaptive temperature control of a water bath system in particular. The NFIN is inherently a modified TSK (Takagi-Sugeno-Kang)-type fuzzy rule-based model possessing neural network's learning ability. In contrast to the general adaptive neural fuzzy networks, where the rules should be decided in advance before parameter learning is performed, there are no rules initially in the NFIN. The rules in the NFIN are created and adapted as on-line learning proceeds via simultaneous structure and parameter identification. The NFIN has been applied to a water bath temperature control system. As compared to the BPNN under the same training procedure, the control results show that not only can the NFIN greatly reduce the training time and avoid the overtuned phenomenon, but the NFIN also has perfect regulation ability. (C) 2000 Elsevier Science B.V. All rights reserved.