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🏛️國立陽明交通大學機構典藏

Author: search.filters.author.Lin, CTSubject: search.filters.subject.membership function

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    A GA-based fuzzy adaptive learning control network
    (2000-05-16)
    Chung, IF
    ;
    Lin, CJ
    ;
    Lin, CT
    ;
    電控工程研究所
    ;
    Institute of Electrical and Control Engineering
    This paper addresses the structure and an associated learning algorithm of a feedforward multilayered connectionist network for realizing the basic elements and functions of a traditional fuzzy logic controller. The proposed fuzzy adaptive learning control network (FALCON) can be contrasted with the traditional fuzzy logic control systems in their network structure and learning ability. A structure/parameter learning algorithm, called FALCON-GA, is proposed for constructing the FALCON automatically. The FALCON-GA is a three-phase hybrid learning algorithm. In the first phase, the fuzzy ART algorithm is used to do fuzzy clustering in the input/output spaces according to the supervised training data. In the second phase, the genetic algorithm (GA) is used to find proper fuzzy logic rules by associating input clusters and output clusters. Finally, in the third phase, the backpropagation algorithm is used for tuning input:output membership functions. Hence, the FALCON GA combines the backpropagation algorithm for parameter learning and both the fuzzy ART and GAs for structure learning. It can partition the input/output spaces, tune membership functions and find proper fuzzy logic rules automatically. The proposed FALCON has two important features. First, it reduces the combinatorial demands placed by the standard methods for adaptive linearization of a system. Second, the FALCON is a highly autonomous system. In its learning scheme, only the training data need to be provided from the outside world. The users need not give the initial fuzzy partitions, membership functions and fuzzy logic rules. Computer simulations have been conducted to illustrate the performance and applicability of the proposed system. (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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    A neural fuzzy system for image motion estimation
    (2000-09-01)
    Lin, CT
    ;
    Chung, IF
    ;
    Sheu, LKM
    ;
    電控工程研究所
    ;
    Institute of Electrical and Control Engineering
    Many methods for computing optical flow (image motion vector) have been proposed while others continue to appear. Block-matching methods are widely used because of their simplicity and easy implementation. The motion vector is uniquely defined, in block-matching methods, by the best fit of a small reference subblock from a previous image frame in a larger, search region from the present image frame. Hence, this method is very sensitive to the real environments (involving occlusion, specularity, shadowing, transparency, etc.). In this paper, a neural fuzzy system with robust characteristics and learning ability is incorporated with the block-matching method to make a system adaptive for different circumstances. In the neural fuzzy motion estimation system, each subblock in the search region is assigned a similarity membership contributing different degrees to the motion vector. This system is more reliable, robust, and accurate in motion estimation than many other methods including Horn and Schunck's optical flow, fuzzy logic motion estimator (FME), best block matching, NR, and fast block matching. Since fast block-matching algorithms can be used to reduce search time, a three-step fast search method is employed to find the motion vector in our system. However, the candidate motion vector is often trapped by the local minimum, which makes the motion vector undesirable. An improved three-step fast search method is tested to reduce the effect from local minimum and some comparisons about fast search algorithms are made. In addition, a Quarter Compensation Algorithm for compensating the interframe image to tackle the problem that the motion vector is not an integer but rather a floating point is proposed. Since our system can give the accurate motion vector, we may use the motion information in many different applications such as motion compensation, CCD camera auto-focusing or zooming, moving object extraction, etc. Two application examples will be illustrated in this paper. (C) 2000 Elsevier Science B.V. All rights reserved.
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    Improvement of machining accuracy by fuzzy logic at corner parts for wire-EDM
    (2001-09-16)
    Lin, CT
    ;
    Chung, IF
    ;
    Huang, SY
    ;
    電控工程研究所
    ;
    Institute of Electrical and Control Engineering
    Wire electrical discharge machining (wire-EDM) has always occupied an important position in some production fields, due to its capability of machining hard materials and intricate shapes. However, the machining accuracy, especially at corner parts, may be destroyed because of some phenomena such as wire deflection and vibration, etc. The purpose of this paper is to develop a control strategy based on fuzzy logic so that the machining accuracy at corner parts for wire-EDM can be improved. The fuzzy rules based on the wire-EDM's physical characteristics, experimental data, and operator's experience are constructed, so that the reduced percentage of sparking force can be determined by a multi-variables fuzzy logic controller. The objective of the total control is to improve the machining accuracy at corner parts, but still keep the cutting feedrate at fair values. As a result of experiments, machining errors of corner parts, especially in rough-cutting, can be reduced to less than 50% of those in normal machining, while the machining process time increases not more than 10% of the normal value. (C) 2001 Elsevier Science B.V. All rights reserved.