Peter Kwong-Shun Tam

Tuning of the structure and parameters of a neural network using an improved genetic algorithm

This paper presents the tuning of the structure and parameters of a neural network using an improved genetic algorithm (GA). It is also shown that the improved GA performs better than the standard GA based on some benchmark test functions. A neural network with switches introduced to its links is proposed. By doing this, the proposed neural network can learn both the input-output relationships of an application and the network structure using the improved GA. The number of hidden nodes is chosen manually by increasing it from a small number until the learning performance in terms of fitness value is good enough. Application examples on sunspot forecasting and associative memory are given to show the merits of the improved GA and the proposed neural network.

An iterative algorithm for minimum cross entropy thresholding

A fast iterative method is derived for minimum cross entropy thresholding using a one-point iteration scheme. Simulations performed using synthetic generated histograms and a real image show the speed advantage and the accuracy of the iterated version.

Modification of hough transform for circles and ellipses detection using a 2-dimensional array

Abstract The Hough transform is a robust technique which is useful in detecting straight lines in an edge-enhanced picture. However, the extension of the conventional Hough transform to recover circles and ellipses has been limited by slow speed and excessive memory. This paper presents techniques aimed at improving the efficiency and reducing the memory size of the accumulator array. Based on these techniques, only a 2-dimensional array is needed for the detection of circles and ellipses. The approach centres on the use of parallel edge points and a method on reducing the dimension of the accumulator array.

A fuzzy sliding controller for nonlinear systems

It is well known that sliding-mode control can give good transient performance and system robustness. However, the presence of chattering may introduce problems to the actuators. Many chattering elimination methods use a finite DC gain controller which leads to a finite steady-state error. One method to ensure zero steady-state error is using a proportional plus integral (PI) controller. This paper proposes a fuzzy logic controller which combines a sliding-mode controller (SMC) and a PI controller. The advantages of the SMC and the PI controller can be combined and their disadvantages can be removed. The system stability is proved, although there is one more state variable to be considered in the PI subsystem. An illustrative example shows that good transient and steady-state responses can be obtained by applying the proposed controller.

The control of switching DC-DC converters-a general LWR problem

The control of switching DC-DC converters is reviewed. It is regarded as a general linear quadratic regulator (LQR) problem, and an innovative optimal and robust digital controller is proposed. The control strategy adopted can achieve good regulation, rejection of modest disturbances, and the ability to cater to switching converters with RHP zeros. This controller design is a general approach that is applicable to all PWM-type DC-DC converters with their circuit topologies known or unknown. Modern CAD techniques are used to reach the final control law. Application to a published Cuk converter is used as an example, and the performance is evaluated. >

A novel genetic-algorithm-based neural network for short-term load forecasting

This paper presents a neural network with a novel neuron model. In this model, the neuron has two activation functions and exhibits a node-to-node relationship in the hidden layer. This neural network provides better performance than a traditional feedforward neural network, and fewer hidden nodes are needed. The parameters of the proposed neural network are tuned by a genetic algorithm with arithmetic crossover and nonuniform mutation. Some applications are given to show the merits of the proposed neural network.

Lyapunov-function-based design of fuzzy logic controllers and its application on combining controllers

This paper presents the design of fuzzy logic controllers (FLCs) for nonlinear systems with guaranteed closed-loop stability and its application on combining controllers. The design is based on heuristic fuzzy rules. Although each rule in the FLC refers to a stable closed-loop subsystem, the overall system stability cannot be guaranteed when all these rules are applied together. In this paper, it is proved that if each subsystem is stable in the sense of Lyapunov (ISL) under a common Lyapunov function, the overall system is also stable ISL. Since no fuzzy plant model is involved, the number of subsystems generated is relatively small, and the common Lyapunov function can be found more easily. To probe further, an application of this design approach to an inverted pendulum system that combines a sliding-mode controller (SMC) and a state feedback controller (SFC) is reported. Each rule in this FLC has an SMC or an SFC in the consequent part. The role of the FLC is to schedule the final control under different antecedents. The stability of the whole system is guaranteed by the proposed design approach. More importantly, the controller thus designed can keep the advantages and remove the disadvantages of the two conventional controllers.

An improved LQR-based controller for switching DC-DC converters

A general approach for controlling pulse-width-modulated (PWM) -type switching DC-DC converters digitally using state-feedback techniques and linear optimal control theory is reported. The methodology for redesigning the state estimator is investigated, and a method derived from the general linear-quadratic-regulator (LQR) problem, is proposed. The method is found to offer better transient responses and robustness to uncertainties in plant parameters when compared with the typical eigenvalue-assignment method. Special attention is directed to plant models with possible migrations of the open-loop zeroes across the stability boundary during operation. Results of applying these techniques to a published Cuk converter are reported to illustrate different points of interest. >

A practical fuzzy logic controller for the path tracking of wheeled mobile robots

This article tackles the path-tracking problem of wheeled mobile robots (WMRs) used in the Micro Robot Soccer Tournament (MicroSot). An heuristic fuzzy logic controller (FLC) has been designed based on a model-free approach. Hardware experimental results have been presented to verify that the FLC can control a WMR. The performance of a fine-tuned P-controller has been compared with that of the proposed fuzzy logic controller. The response time of the fuzzy-logic-controlled WMR is two times faster. Good tracking control performance was also obtained from the proposed controller.

Short-term electric load forecasting based on a neural fuzzy network

Electric load forecasting is essential to improve the reliability of the ac power line data network and provide optimal load scheduling in an intelligent home system. In this paper, a short-term load forecasting realized by a neural fuzzy network (NFN) and a modified genetic algorithm (GA) is proposed. It can forecast the hourly load accurately with respect to different day types and weather information. By introducing new genetic operators, the modified GA performs better than the traditional GA under some benchmark test functions. The optimal network structure can be found by the modified GA when switches in the links of the network are introduced. The membership functions and the number of rules of the NFN can be obtained automatically. Results for a short-term load forecasting will be given.