Yu-Yi Fu

Identification of MIMO systems using radial basis function networks with hybrid learning algorithm

When a radial basis function network (RBFN) is used for identification of a nonlinear multi-input multi-output (MIMO) system, the number of hidden layer nodes, the initial parameters of the kernel, and the initial weights of the network must be determined first. For this purpose, a systematic way that integrates the support vector regression (SVR) and the least squares regression (LSR) is proposed to construct the initial structure of the RBFN. The first step of the proposed method is to determine the number of hidden layer nodes and the initial parameters of the kernel by the SVR method. Then the weights of the RBFN are determined by solving a simple minimization problem based on the concept of LSR. After initialization, an annealing robust learning algorithm (ARLA) is then applied to train the RBFN. With the proposed initialization approach, one can find that the designed RBFN has few hidden layer nodes while maintaining good performance. To show the feasibility and superiority of the annealing robust radial basis function networks (ARRBFNs) for identification of MIMO systems, several illustrative examples are included.

ARFNNs with SVR for prediction of chaotic time series with outliers

This paper demonstrates an approach to predict the chaotic time series with outliers using annealing robust fuzzy neural networks (ARFNNs). A combination model that merges support vector regression (SVR), radial basis function networks (RBFNs) and simplified fuzzy inference system is used. The SVR has the good performances to determine the number of rules in the simplified fuzzy inference system and initial weights for the fuzzy neural networks (FNNs). Based on these initial structures, and then annealing robust learning algorithm (ARLA) can be used effectively to overcome outliers and adjust the parameters of structures. Simulation results show the superiority of the proposed method with different SVR for training and prediction of chaotic time series with outliers.

Radial basis function networks with hybrid learning for system identification with outliers

This paper demonstrates that radial basis function networks (RBFNs) with support vector regression (SVR) and annealing robust learning algorithm (ARLA) can be used effectively for the identification of the nonlinear dynamic systems with outliers. When the RBFNs are used for the identification of the nonlinear dynamic system, the number of hidden nodes, the initial parameters of the kernel, and the initial weights of the network must be determined first, a SVR approach is proposed to solve the initial problem of RBFNs. That is, the SVR uses the quadratic programming optimization to determine the initial structure of the RBFNs. Besides, the new cost function for the system identification with outliers is also proposed. That is, the proposed annealing robust radial basis function networks (ARRBFNs) are trained by the ARLA, which uses the annealing concept in the cost function of the robust back-propagation learning algorithm, can overcome the error measurement caused by the outliers. Simulation results show the superiority of the proposed method with different SVR.

ARRBFNs with SVR for prediction of chaotic time series with outliers

In this article, annealing robust radial basis function networks (ARRBFNs), which consist of a radial basis function network and a support vector regression (SVR), and an annealing robust learning algorithm (ARLA) are proposed for the prediction of chaotic time series with outliers. In order to overcome the initial structural problems of the proposed neural networks, the SVR is utilized to determine the number of hidden nodes, the initial parameters of the kernel, and the initial weights for the proposed ARRBFNs. Then the ARLA that can conquer the outliers is applied to tune the parameters of the kernel and the weights in the proposed ARRBFNs under the initial structure with SVR. The simulation results of Mackey-Glass time series show that the proposed approach with different SVRs can cope with outliers and give a fast learning speed. The results of the simulation are also given to demonstrate the validity of proposed method for chaotic time series with outliers.

A Nonlinear programming method for time-optimal control of an omni-directional mobile robot

The time-optimal control problem of a three-wheeled omni-directional mobile robot is studied in this paper. In contrast to traditional methods, in which the Pontryagins Minimum Principle is usually used, an iterative procedure is proposed to transform the time-optimal problem into a nonlinear programming (NLP) one. In the formulated NLP problem, the number of control steps is fixed initially and the sampling period is treated as a variable to be minimized in the optimization process. An upper limit on the sampling period is set in advance considering the accuracy of discretization. If the value of the sampling period is larger than the upper limit, then an update of the number of control steps is needed. To generate initial feasible solutions of the NLP problem, a genetic algorithm is adopted. Since different initial feasible solutions can be generated, the optimization process can be started from different points to find the optimal solution. In this manner, one can find a time-optimal movement of the omni-directional mobile robot between two configurations. To show the feasibility of the proposed method, simulation results are included for illustration.

Annealing Robust Radial Basis Function Networks with Support Vector Regression for Nonlinear Inverse System Identification with Outliers

In this paper, the proposed annealing robust radial basis function networks (RBFNs) based on support vector regression (SVR), RBFNs and annealing robust learning algorithm (ARLA) for the nonlinear inverse system identification with outliers. That is, a two-stage structure; namely, an identification stage and an inverse identification stage is proposed. Firstly, the ε or v SVR uses the quadratic programming optimization to determine the initial structure of the annealing robust RBFNs in each stage for the system identification and the inverse system identification with outliers. Then, the proposed annealing robust RBFNs are trained by the ARLA, which uses the annealing concept in the cost function of robust back-propagation learning algorithm, can overcome the error measurement caused by the outliers for each stage. Hence, the proposed method has the same capability of universal approximator with the traditional RBFNs, a faster learning speed than the traditional RBFNs and outlier noise rejection with the proposed neural network for the nonlinear inverse system identification with outliers. Finally, the proposed approach is applied to magneto-rheological damper systems. Simulation results show the superiority of the proposed method for the prototype magneto-rheological damper and the nonlinear inverse system identification with outliers.

Integration of PSO and GA for optimum design of fuzzy PID controllers in a pendubot system

In this paper, a novel auto-tuning method is proposed to design fuzzy PID controllers for asymptotical stabilization of a pendubot system. In the proposed method, a fuzzy PID controller is expressed in terms of fuzzy rules, in which the input variables are the error signals and their derivatives, while the output variables are the PID gains. In this manner, the PID gains are adaptive and the fuzzy PID controller has more flexibility and capability than the conventional ones with fixed gains. To tune the fuzzy PID controller simultaneously, an evolutionary learning algorithm integrating particle swarm optimization (PSO) and genetic algorithm (GA) methods is proposed. The simulation results illustrate that the proposed method is indeed more efficient in improving the asymptotical stability of the pendubot system.

Parameter estimation of chaotic systems by a nonlinear time-varying evolution PSO method

An important issue in nonlinear science is parameter estimation for Lorenz chaotic systems. There has been increasing interest in this issue in various research fields, and it could essentially be formulated as a multidimensional optimization problem. A novel evolutionary computation algorithm, nonlinear time-varying evolution particle swarm optimization (NTVEPSO), is employed to estimate these parameters. In the NTVEPSO method, the nonlinear time-varying evolution functions are determined by using matrix experiments with an orthogonal array, in which a minimal number of experiments would have an effect that approximates tothe full factorial experiments. The NTVEPSO method and other PSO methods are then applied to identify the Lorenz chaotic system. Simulation results demonstrate the feasibility and superiority of the proposed NTVEPSO method.

A time-scaling method for near-time-optimal control of an omni-directional robot along specified paths

This paper proposes a time-scaling method to determine the near-time-optimal movement of an omnidirectional mobile robot along a given reference path. With this strategy, the positions of the trajectory after scaling are the same as the original ones such that the geometric path constraints are not violated. However, the velocities and the accelerations are adjusted to meet the dynamical constraints and to minimize the traveling time. When determining the time-scaling function, a cubic spline interpolation technique is used, in which control points for interpolation are determined simultaneously by a particle swarm optimization (PSO) method based on the integration of a time-scaling function. To show the feasibility of the proposed method, the results of a simulation example is illustrated.

A PSO method with nonlinear time-varying evolution for optimal design of PID controllers in a pendubot system

A particle swarm optimization method with nonlinear time-varying evolution (PSO-NTVE) is employed in designing an optimal PID controller for asymptotic stabilization of a pendubot system. In the PSO-NTVE method, parameters are determined by using matrix experiments with an orthogonal array, in which a minimal number of experiments would have an effect that approximates the full factorial experiments. The PSO-NTVE method and other PSO methods are then applied to design an optimal PID controller in a pendubot system. Comparing the simulation results, the feasibility and the superiority of the PSO-NTVE method are verified.