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Author: search.filters.author.Lin, CTSubject: search.filters.subject.neural fuzzy network

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Now showing 1 - 7 of 7
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    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.
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    Direction of arrival estimation based on phase differences using neural fuzzy network
    (2000-07-01)
    Shieh, CS
    ;
    Lin, CT
    ;
    電控工程研究所
    ;
    Institute of Electrical and Control Engineering
    A new high-resolution direction of arrival (DOA) estimation technique using a neural fuzzy network based on phase difference (PD) is proposed in this paper. The conventional DOA estimation method such as MUSIC and MLE, are computationally intensive and difficult to implement in real time. To attach these problems, neural networks have become popular for DOA estimation in recent years. However, the normal neural networks such as multilayer perceptron (MLP) and radial basis function network (RBFN) usually produce the extra problems of low convergence speed and/or large network size (i.e., the number of network parameters is large). Also, the way to decide the network structure is heuristic. To overcome these defects and take use of neural learning ability, a powerful self-constructing neural fuzzy inference network (SONFIN) is used to develop a new DOA estimation algorithm in this paper. By feeding the PD's of received radar-array signals, the trained SONFIN can give high-resolution DOA estimation. The proposed scheme is thus called PD-SONFIN, This new algorithm avoids the need of empirically determining the network size and parameters in normal neural networks due to the powerful on-line structure and parameter learning ability of SONFIN, The PD-SONFIN can always find itself an economical network size in fast learning process. Our simulation results show that the performance of the new algorithm is superior to the RBFN in terms of convergence accuracy, estimation accuracy, sensitivity to noise, and network size.
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    Word boundary detection with mel-scale frequency bank in noisy environment
    (2000-09-01)
    Wu, GD
    ;
    Lin, CT
    ;
    電控工程研究所
    ;
    Institute of Electrical and Control Engineering
    This paper addresses the problem of automatic word boundary detection in the presence of noise. We first propose an adaptive time-frequency (ATF) parameter for extracting both the time and frequency features of noisy speech signals. The ATF parameter extends the TF parameter proposed by Junqua et al. from Single band to multiband spectrum analysis, where the frequency bands help to make the distinction of speech and noise signals clear. The ATF parameter can extract useful frequency information by adaptively choosing proper bands of the mel-scale frequency bank. The ATF parameter increased the recognition rate by about 3% of a TF-based robust algorithm which has been shown to outperform several commonly used algorithms for word boundary detection in the presence of noise. The ATF parameter also reduced the recognition error rate due to endpoint detection to about 20%. Based on the ATF parameter, we further propose a new word boundary detection algorithm by using a neural fuzzy network (called SONFIN) for identifying islands of word signals in noisy environment. Due to the self-learning ability of SONFIN, the proposed algorithm avoids the need of empirically determining thresholds and ambiguous rules in normal word boundary detection algorithms. As compared to normal neural networks, the SONFIN can always find itself an economic network size in high learning speed. Our results also showed that the SONFIN's performance is not significantly affected by the size of training set. The ATF-based SONFIN achieved higher recognition rate than the TF-based robust algorithm by about 5%. It also reduced the recognition error rate due to endpoint detection to about 10%, compared to an average of approximately 30% obtained with the TF-based robust algorithm, and 50% obtained with the modified version of the Lamel ct al. algorithm.
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    Noisy speech segmentation/enhancement with multiband analysis and neural fuzzy networks
    (2002-11-01)
    Lin, CT
    ;
    Wu, RC
    ;
    Wu, GD
    ;
    電控工程研究所
    ;
    Institute of Electrical and Control Engineering
    This paper addresses the problem of speech segmentation and enhancement in the presence of noise. We first propose a new word boundary detection algorithm by using a neural fuzzy network (called ATF-based SONFIN algorithm) for identifying islands of word signals in fixed noise-level environment. We further propose a new RTF-based RSONFIN algorithm where the background noise level varies during the procedure of recording. The adaptive time-frequency (ATF) and refined time-frequency (RTF) parameters extend the TF parameter from single band to multiband spectrum analysis, and help to make the distinction of speech and noise signals clear. The ATF and RTF parameters can extract useful frequency information by adaptively choosing proper bands of the mel-scale frequency bank. Due to the self-learning ability of SONFIN and RSONFIN, the proposed algorithms avoid the need of empirically determining thresholds and ambiguous rules. The RTF-based RSONFIN algorithm can also find the variation of the background noise level and detect correct word boundaries in the condition of variable background noise level by processing the temporal relations. Our experimental results show that both in the fixed and variable noise-level environment, the algorithms that we proposed achieved higher recognition rate than several commonly used word boundary detection algorithms and reduced the recognition error rate due to endpoint detection.
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    Tracking a maneuvering target using neural fuzzy network
    (2004-02-01)
    Duh, FB
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    Lin, CT
    ;
    電控工程研究所
    ;
    Institute of Electrical and Control Engineering
    A fast target maneuver detecting and highly accurate tracking technique using a neural fuzzy network based on Kalman filter is proposed in this paper. In the automatic target tracking system, there exists an important and difficult problem: how to detect the target maneuvers and fast response to avoid misstracking? The traditional maneuver detection algorithms, such as variable dimension filter (VDF) and input estimation (IE) etc., are computation intensive and difficult to implement in real time. To solve this problem, neural network algorithms have been issued recently. However, the normal neural networks such as backpropagation networks usually produce the extra problems of low convergence speed and/or large network size. Furthermore, the way to decide the network structure is heuristic. To overcome these defects and to make use of neural learning ability, a developed standard Kalman filter with a self-constructing neural fuzzy inference network (KF-SONFIN) algorithm for target tracking is presented in this paper. By generating possible target trajectories including maneuver information to train the SONFIN, the trained SONFIN can detect when the maneuver occurred, the magnitude of maneuver values and when the maneuver disappeared. Without having to change the structure of Kalman filter nor modeling the maneuvering target, this new algorithm, SONFIN, can always find itself an economic network size with a fast learning process. Simulation results show that the KF-SONFIN is superior to the traditional IE and VDF methods in estimation accuracy.
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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.
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    Adaptive feature extractions in an EEG-based alertness estimation system
    (2005)
    Lin, CT
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    Chao, WH
    ;
    Chen, YC
    ;
    Liang, SF
    ;
    腦科學研究中心
    During the past years, the public security has become an important issue, especially, the safe manipulation and control of various vehicles. Maintaining high cognition is particularly important for the drivers behind the steering wheel. It requires an optimal estimation system to online continuously detect drivers' cognitive state related to abilities in perception, recognition, and vehicle control. In this paper, we proposed an EEG-based alertness estimation system with automatic feature selection mechanism. The independent component analysis (ICA) is used first to decompose the measured electroencephalogram (EEG). Then, a time-frequency analysis is performed to evaluate the time-frequency characteristic of each ICA component. We also proposed a new adaptive feature extracting mechanism for selections of frequency bands and ICA components. Different ranges of the alpha rhythm of subjects can be evaluated by the adaptive feature extracting mechanism according to the correlation coefficient between the ICA time-frequency response and the driving performance. The extracted features are then trained both by linear regression model and Self-cOnstructing Neuro-Fuzzy Inference Network (SONFIN) for the estimation of driving performance. The training and testing results of SONFIN are 96% and 91%, while the results of linear regression model are 90% and 85%, respectively. It demonstrates that the proposed adaptive feature extracting mechanism can achieve a great performance in alertness estimation with frequency band and component selection.