Browsing by Author "LEE, CSG"

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A MULTIVALUED BOLTZMANN MACHINE
(1995-04-01)
LIN, CT
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LEE, CSG
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電控工程研究所
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Institute of Electrical and Control Engineering
The idea of Hopfield network is based on the Ising spin glass model in which each spin has only two possible states: up and down. By introducing stochastic factors into this network and performing a simulated annealing process on it, it becomes a Boltzmann machine which can escape from local minimum states to achieve the global minimum. This paper generalizes the above ideas to multi-value case based on the XY spin glass-model in which each spin can be in any direction in a plane, Simply using the gradient descent method and the analog Hopfield network; two different analog connectionist structures and their corresponding evolving rules are first designed to transform the XY spin glass model to distributed computational models. These two analog computational models are single-layered connectionist structures and multi-layered Hopfield analog networks. The latter network eases the node (neuron) computational requirement of the former at the expense of more neurons and connections. With the proposed evolving rules, the proposed models evolve according to a predefined Hamiltonian (energy function) which will decrease until it reaches a (perhaps local) minimum. Since these two structures can easily get stuck in local minima, a multi-valued Boltzmann machine is proposed which adopts the discrete planar spin glass model for the local minimum problem. Each neuron in the multi-valued Boltzmann machine can only take n discrete directions (states). The stochastic simulated annealing method is introduced to the evolving rules of the multi-valued Boltzmann machine to solve the local minimum problem. The multi-valued Boltzmann machine can be applied to the mobile robot navigation problem;problem by defining proper artificial magnetic field on the traverse terrain. This new artificial magnetic field approach for the mobile robot navigation problem has shown to have several advantages over existing graph search and potential field techniques.
FUZZY ADAPTIVE LEARNING CONTROL NETWORK WITH ONLINE NEURAL LEARNING
(1995-04-14)
LIN, CT
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LIN, CJ
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LEE, CSG
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電控工程研究所
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Institute of Electrical and Control Engineering
This paper addresses the structure and the associated on-line learning algorithms 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. The connectionist structure of the proposed FALCON can be constructed from training examples by neural learning techniques to find proper fuzzy partitions, membership functions, and fuzzy logic rules. Two complementary on-line structure/parameter learning algorithms, FALCON-FSM and FALCON-ART, are proposed for constructing the FALCON dynamically. The FALCON-FSM combines the backpropagation learning scheme for parameter learning and a fuzzy similarity measure for structure learning. The FALCON-FSM can find proper fuzzy logic rules, membership functions, and the size of output partitions simultaneously. In the FALCON-FSM algorithm, the input and output spaces are partitioned into ''grids''. The grid-typed space partitioning certainly makes both the fuzzy logic controller software emulation and fuzzy chip implementation convenient. However, as the number of input/output variables increases, the number of partitioned grids will grow combinatorially. To avoid the problem of combinatorial growth of partitioned grids in some complex systems, the FALCON-ART algorithm is developed, which can partition the input and output spaces in a more flexible way based on the distribution of the training data. The FALCON-ART combines the backpropagation learning scheme for parameter learning and a fuzzy ART algorithm for structure learning. The FALCON-ART can on-line partition the input and output spaces, tune membership functions and find proper fuzzy logic rules dynamically. Computer simulations were conducted to illustrate the performance and applicability of both FALCON-FSM and FALCON-ART learning algorithms.
REINFORCEMENT STRUCTURE PARAMETER LEARNING FOR NEURAL-NETWORK-BASED FUZZY-LOGIC CONTROL-SYSTEMS
(1994-02-01)
LIN, CT
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LEE, CSG
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電控工程研究所
;
Institute of Electrical and Control Engineering
This paper proposes a reinforcement neural-network-based fuzzy logic control system (RNN-FLCS) for solving various reinforcement learning problems. The proposed RNN-FLCS is constructed by integrating two neural-network-based fuzzy logic controllers (NN-FLC's), each of which is a connectionist model with a feedforward multilayered, network developed for the realization of a fuzzy logic controller. One NN-FLC performs as a fuzzy predictor, and the other as a fuzzy controller. Using the temporal difference prediction method, the fuzzy predictor can predict the external reinforcement signal and provide a more informative internal reinforcement signal to the fuzzy controller. The fuzzy controller performs a stochastic exploratory algorithm to adapt itself according to the internal reinforcement signal. During the learning process, both structure learning and parameter learning are performed simultaneously in the two NN-FLC's using the fuzzy similarity measure. The proposed RNN-FLCS can construct a fuzzy logic control and decision-making system automatically and dynamically through a reward/penalty signal (i.e., a ''good'' or ''bad'' signal) or through very simple fuzzy information feedback such as ''high,'' ''too high,'' ''low,'' and ''too low.'' The proposed RNN-FLCS is best applied to the learning environment, where obtaining exact training data is expensive. The proposed RNN-FLCS also preserves the advantages of the original NN-FLC, such as the ability to find proper network structure and parameters simultaneously and dynamically and to avoid the rule-matching time of the inference engine in the traditional fuzzy logic systems. Computer simulations were conducted to illustrate the performance and applicability of the proposed RNN-FLCS.
RESIDUE ARITHMETIC VLSI ARRAY ARCHITECTURE FOR MANIPULATOR PSEUDO-INVERSE JACOBIAN COMPUTATION - REPLY
(1991-10-01)
CHANG, PP
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LEE, CSG
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電信工程研究所
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Institute of Communications Engineering