Ming-Feng Yeh

Grey particle swarm optimization

With the help of grey relational analysis, this study attempts to propose two grey-based parameter automation strategies for particle swarm optimization (PSO). One is for the inertia weight and the other is for the acceleration coefficients. By the proposed approaches, each particle has its own inertia weight and acceleration coefficients whose values are dependent upon the corresponding grey relational grade. Since the relational grade of a particle is varying over the iterations, those parameters are also time-varying. Even if in the same iteration, those parameters may differ for different particles. In addition, owing to grey relational analysis involving the information of population distribution, such parameter automation strategies make an attempt on the grey PSO to perform a global search over the search space with faster convergence speed. The proposed grey PSO is applied to solve the optimization problems of 12 unimodal and multimodal benchmark functions for illustration. Simulation results are compared with the adaptive PSO (APSO) and two well-known PSO variants, PSO with linearly varying inertia weight (PSO-LVIW) and PSO with time-varying acceleration coefficients (HPSO-TVAC), to demonstrate the search performance of the grey PSO.

Standalone CMAC Control System With Online Learning Ability

A cerebellar model articulation controller (CMAC) control system, which contains only one single-input controller implemented by a differentiable CMAC, is proposed in this paper. In the proposed scheme, the CMAC controller is solely used to control the plant, and no conventional controller is needed. Without a preliminary offline learning, the single-input CMAC controller can provide the control effort to the plant at each online learning step. To train the differentiable CMAC online, the gradient descent algorithm is employed to derive the learning rules. The sensitivity of the plant, with respect to the input, is approximated by a simple formula so that the learning rules can be applied to unknown plants. Moreover, based on a discrete-type Lyapunov function, conditions on the learning rates guaranteeing the convergence of the output error are derived in this paper. Finally, simulations on controlling three different plants are given to demonstrate the effectiveness of the proposed controller.

Single-input CMAC control system

This paper attempts to propose a single-input cerebellar model articulation controller (CMAC) control system, which contains only one controller implemented by the CMAC. The single-input CMAC control system adopts two learning stages. An off-line learning stage is to enable the output behavior of the CMAC to approximate the control surface of a fuzzy PD-type controller. An on-line learning stage follows to improve the system stability by the modified learning rule. The linear interpolation scheme is also applied to the recall process at the on-line learning stage to ensure better accuracy of the CMAC output. Simulation results show that the single-input CMAC controller is superior to the fuzzy PD-type controller.

A self-organizing CMAC network with gray credit assignment

This paper attempts to incorporate the structure of the cerebellar-model-articulation-controller (CMAC) network into the Kohonen layer of the self-organizing map (SOM) to construct a self-organizing CMAC (SOCMAC) network. The proposed SOCMAC network can perform the function of an SOM and can distribute the learning error into the memory contents of all addressed hypercubes as a CMAC. The learning of the SOCMAC is in an unsupervised manner. The neighborhood region of the SOCMAC is implicit in the structure of a two-dimensional CMAC network and needs not be defined in advance. Based on gray relational analysis, a credit-assignment technique for SOCMAC learning is introduced to hasten the overall learning process. This paper also analyzes the convergence properties of the SOCMAC. It is shown that under the proposed updating rule, both the memory contents and the state outputs of the SOCMAC converge almost surely. The SOCMAC is applied to solve both data-clustering and data-classification problems, and simulation results show that the proposed network achieves better performance than other known SOMs.

A PI-like fuzzy controller implementation for the inverted pendulum system

A PI-like fuzzy controller is designed and implemented for the inverted pendulum system. First, the proposed fuzzy controller adopts the step response phase trajectories to derive the corresponding linguistic control rules of such a system, then applies the relationship between input and output signals in a proportional integral controller to convert the relationship of linguistic control rules to a decision table. At the same time, the membership functions shape is mapped and tuned with constant proportional gain and constant integral gain. Finally, the fuzzy controller of the inverted pendulum system is accomplished by the designed decision table and the determined membership function. By using the Single Board Fuzzy Controller, made by Togai InfraLogic Inc., the proposed methods are proved.

Particle swarm optimization with grey evolutionary analysis

Based on grey relational analysis, this study attempts to propose a grey evolutionary analysis (GEA) to analyze the population distribution of particle swarm optimization (PSO) during the evolutionary process. Then two GEA-based parameter automation approaches are developed. One is for the inertia weight and the other is for the acceleration coefficients. With the help of the GEA technique, the proposed parameter automation approaches would enable the inertia weight and acceleration coefficients to adapt to the evolutionary state. Such parameter automation behaviour also makes an attempt on the GEA-based PSO to perform a global search over the search space with faster convergence speed. In addition, the proposed PSO is applied to solve the optimization problems of twelve unimodal and multimodal benchmark functions for illustration. Simulation results show that the proposed GEA-based PSO could outperform the adaptive PSO, the grey PSO, and two well-known PSO variants on most of the test functions.

Design and analysis of direct-action CMAC PID controller

This paper is to propose a direct-action (DA) cerebellar model articulation controller (CMAC) proportional-integral-derivative (PID) controller. The proposed controller, termed the DAC-PID controller, can generate four simple types of the nonlinear functions and then determine a control effort from those functions to control the process. In addition, the real-coded genetic algorithm is used to tune the parameters of the DAC-PID controller such that we can optimize those parameters. The performance of the proposed controller is also discussed in the sense of quantitative analysis. Simulation results demonstrate that the DAC-PID controller is superior to the conventional PID controller tuned by Ziegler-Nichols method and, moreover, as better as the optimal PID controller and the optimal fuzzy-PID controller.

Design of a hybrid adaptive CMAC with supervisory controller for a class of nonlinear system

This paper attempts to propose a hybrid adaptive cerebellar model articulation controller (CMAC) sliding mode control (SMC; called HAC-SMC) with a supervisory controller for a class of nonlinear system, in which the HAC composed of a direct adaptive CMAC and an indirect adaptive CMAC control is performed as the SMC. There are two methods to design the switching control law of SMC. One is the sign switching controller. The other is the CMAC switching controller. Besides, a supervisory controller is appended to the HAC-SMC to guarantee the states staying in the boundary layer. The adaptive laws of the control system are derived in the sense of Lyapunov theorem so that the asymptotic stability of the system could be guaranteed. Simulation results show that the proposed control system has satisfactory performance on the inverted pendulum system and the Chuas chaotic circuit.

On-line adaptive quantization input space in CMAC neural network

Cerebellar model articulation controller (CMAC) is one kind of neural network that imitates the human cerebellum. It has attractive properties of learning ability and generalization capability. However, the conventional CMAC with equal-size quantization cannot well represent the variation of the target function by finite knots. This paper proposes an online adaptive quantization method that is utilized to adaptively partition the input space of CMAC in accordance with the grey relational analysis. Simulation results on the function approximation show that our method performs better than the conventional one in both the learning speed and the learning precision.

Evaluating weapon systems based on grey relational analysis and fuzzy arithmetic operations

Abstract In this paper, we propose a new and simple method for evaluating weapon systems by an analytic hierarchy process based on grey relational analysis and fuzzy arithmetic operations, which is a multiple criteria decision‐making approach in a fuzzy environment. Since the rank score matrix of the proposed method is generated by the grey relational analysis, it provides a systematic approach to describe the rank score of each criterion item. Also because the proposed method utilizes the simplified fuzzy arithmetic operations of fuzzy numbers rather than the fuzzy scales method or the complicated entropy weight calculations, its execution time is shorter than the previous methods. An example of tactical missile systems selection is utilized to illustrate the performance evaluation process of a weapon system. It can be seen that the simulation result is coincident with the conventional methods.