Adaptive fuzzy neural production network with MIMO-structure for the evaluation of technology efficiency

The paper presents an example of modeling the algorithm operation. The paper analyses the modified Wang and Mendel MIMO-architecture of the adaptive fuzzy neural production network with a logical conclusion. It is distinguished by the automatic generation of a set of fuzzy rules based on a fuzzy decision tree and a hybrid algorithm for parameter adaptation by the neural network (centers, widths of membership functions, and conclusions) starting from the leaf nodes to the root nodes of the tree

Evaluation of a fuzzy-expert system for fault diagnosis in power systems

A major problem with alarm processing and fault diagnosis in power systems is the reliance on the circuit alarm status. If there is too much information available and the time of arrival of the information is random due to weather conditions etc., the alarm activity is not easily interpreted by system operators. In respect of these problems, this thesis sets out the work that has been carried out to design and evaluate a diagnostic tool which assists power system operators during a heavy period of alarm activity in condition monitoring. The aim of employing this diagnostic tool is to monitor and raise uncertain alarm information for the system operators, which serves a proposed solution for restoring such faults. The diagnostic system uses elements of AI namely expert systems, and fuzzy logic that incorporate abductive reasoning. The objective of employing abductive reasoning is to optimise an interpretation of Supervisory Control and Data Acquisition (SCADA) based uncertain messages when the SCADA based messages are not satisfied with simple logic alone. The method consists of object-oriented programming, which demonstrates reusability, polymorphism, and readability. The principle behind employing objectoriented techniques is to provide better insights and solutions compared to conventional artificial intelligence (Al) programming languages. The characteristics of this work involve the development and evaluation of a fuzzy-expert system which tries to optimise the uncertainty in the 16-lines 12-bus sample power system. The performance of employing this diagnostic tool is assessed based on consistent data acquisition, readability, adaptability, and maintainability on a PC. This diagnostic tool enables operators to control and present more appropriate interpretations effectively rather than a mathematical based precise fault identification when the mathematical modelling fails and the period of alarm activity is high. This research contributes to the field of power system control, in particular Scottish Hydro-Electric PLC has shown interest and supplied all the necessary information and data. The AI based power system is presented as a sample application of Scottish Hydro-Electric and KEPCO (Korea Electric Power Corporation)

Prototype expert system for site selection of a sanitary landfill

Author name used in this publication: K. W. ChauTitle on pre-published version: A prototype expert system for site selection of a sanitary landfill2005-2006 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Development of an integrated knowledge-based system on flow and water quality in Hong Kong coastal waters

Author name used in this publication: K. W. Chau2006-2007 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Improving Computer Based Speech Therapy Using a Fuzzy Expert System

In this paper we present our work about Computer Based Speech Therapy systems optimization. We focus especially on using a fuzzy expert system in order to determine specific parameters of personalized therapy, i.e. the number, length and content of training sessions. The efficiency of this new approach was tested during an experiment performed with our CBST, named LOGOMON

An expert fuzzy logic controller employing adaptive learning for servo systems

An expert fuzzy logic controller with adaptive learning is proposed as an intelligent controller for servo systems. A key component of this controller is an adaptive learning mechanism which is used to self-regulate the scaling factors and the control action based on the error between the desired value and the plant output. The inference engine of this controller is based on the principle of approximate reasoning and the learning strategy is based on reinforcement learning. A novel approach of model reference adaptive control is also proposed for servo systems. The comparison of the performance between the proposed controller and PID controllers is discussed. The simulation results show that the performance of the proposed controller is better than the conventional approach or previous research. The real-time application demonstrates that a faster response of a servo system can be achieved. Furthermore, the proposed controller is relatively insensitive to variations in the parameters of control systems

Application of Fuzzy Logic in Job Satisfaction Performance Problem

AbstractJob satisfaction has been a popular topic of research for many decades. The interest in this topic has attracted psychologists, management scholars and, more recently, economists. Most of the studies conducted in the area of job satisfaction have been based on statistical methods. However these methods cannot account for the fact that basic facets of job satisfaction, such as Activity, Independence, Variety, Social status, and Supervision-human relations, to name but a few, are evaluated based on perceptions which do not provide precise numeric information. Information supported by perceptions can be processed more adequately by using fuzzy logic. In this paper we suggest fuzzy if-then rules based expert system to describe relations between job factors and overall job satisfaction