Learning of Type-2 Fuzzy Logic Systems using Simulated Annealing.

This thesis reports the work of using simulated annealing to design more efficient fuzzy logic systems to model problems with associated uncertainties. Simulated annealing is used within this work as a method for learning the best configurations of type-1 and type-2 fuzzy logic systems to maximise their modelling ability. Therefore, it presents the combination of simulated annealing with three models, type-1 fuzzy logic systems, interval type-2 fuzzy logic systems and general type-2 fuzzy logic systems to model four bench-mark problems including real-world problems. These problems are: noise-free Mackey-Glass time series forecasting, noisy Mackey-Glass time series forecasting and two real world problems which are: the estimation of the low voltage electrical line length in rural towns and the estimation of the medium voltage electrical line maintenance cost. The type-1 and type-2 fuzzy logic systems models are compared in their abilities to model uncertainties associated with these problems. Also, issues related to this combination between simulated annealing and fuzzy logic systems including type-2 fuzzy logic systems are discussed. The thesis contributes to knowledge by presenting novel contributions. The first is a novel approach to design interval type-2 fuzzy logic systems using the simulated annealing algorithm. Another novelty is related to the first automatic design of general type-2 fuzzy logic system using the vertical slice representation and a novel method to overcome some parametrisation difficulties when learning general type-2 fuzzy logic systems. The work shows that interval type-2 fuzzy logic systems added more abilities to modelling information and handling uncertainties than type-1 fuzzy logic systems but with a cost of more computations and time. For general type-2 fuzzy logic systems, the clear conclusion that learning the third dimension can add more abilities to modelling is an important advance in type-2 fuzzy logic systems research and should open the doors for more promising research and practical works on using general type-2 fuzzy logic systems to modelling applications despite the more computations associated with it

Time series forecasting using a TSK fuzzy system tuned with simulated annealing

In this paper, a combination of a Takagi-Sugeno fuzzy system (TSK) and simulated annealing is used to predict well known time series by searching for the best configuration of the fuzzy system. Simulated annealing is used to optimise the parameters of the antecedent and the consequent parts of the fuzzy system rules. The results of the proposed method are encouraging indicating that simulated annealing and fuzzy logic are able to combine well in time series prediction

Building Strategic Competency for Six-Sigma Implementation: A Model for Saudi Arabia

Ever since Saudi Arabia became a member of the World Trade Organization (WTO) in 2002, Saudi companies had to take quality seriously because of increased international competition. In recent years six-sigma approach is being gradually adopted in some companies to improve quality and competitiveness. In this paper we develop a model that addresses the organizational and workforce competency of the six-sigma adopters in Saudi Arabia and provide a roadmap for its successful adoption. Our qualitative study reveals that leadership support for strategy and sustainable promotion of six-sigma implementation in Saudi Arabia is lacking, expatriate quality professionals have to convince management about any six-sigma project they want to initiate. From design perspective management needs to focus on SIPOC, training programs, reward system, internal marketing, and building the IT infrastructure for sixsigma. However, there are many positives in support six-sigma in Saudi Arabia, such as, Saudi government’s Vision 2030 for general economic development, availability of trained expatriate quality professionals, Saudi managements proclivity for immediate results which is a hallmark of six-sigma, strong national IT infrastructure, just to name a few. The paper concludes with a prescription on building competency for six-sigma in Saudi Arabia

Short-term prediction of solar energy in Saudi Arabia using automated-design fuzzy logic systems.

Solar energy is considered as one of the main sources for renewable energy in the near future. However, solar energy and other renewable energy sources have a drawback related to the difficulty in predicting their availability in the near future. This problem affects optimal exploitation of solar energy, especially in connection with other resources. Therefore, reliable solar energy prediction models are essential to solar energy management and economics. This paper presents work aimed at designing reliable models to predict the global horizontal irradiance (GHI) for the next day in 8 stations in Saudi Arabia. The designed models are based on computational intelligence methods of automated-design fuzzy logic systems. The fuzzy logic systems are designed and optimized with two models using fuzzy c-means clustering (FCM) and simulated annealing (SA) algorithms. The first model uses FCM based on the subtractive clustering algorithm to automatically design the predictor fuzzy rules from data. The second model is using FCM followed by simulated annealing algorithm to enhance the prediction accuracy of the fuzzy logic system. The objective of the predictor is to accurately predict next-day global horizontal irradiance (GHI) using previous-day meteorological and solar radiation observations. The proposed models use observations of 10 variables of measured meteorological and solar radiation data to build the model. The experimentation and results of the prediction are detailed where the root mean square error of the prediction was approximately 88% for the second model tuned by simulated annealing compared to 79.75% accuracy using the first model. This results demonstrate a good modeling accuracy of the second model despite that the training and testing of the proposed models were carried out using spatially and temporally independent data

Experimental Results using fuzzy c-means and simulated annealing in training and testing phases.

<p>Experimental Results using fuzzy c-means and simulated annealing in training and testing phases.</p

Samples of the fuzzy logic system rules constructed by fuzzy c-means.

<p>Abbreviations “in” and “out” refer to input and output variables respectively while “mf” refers to membership functions.</p

Measured GHI data summary.

<p>Measured GHI data summary.</p

A sample of the data used in this experimentation for Riyadh station.

<p>A sample of the data used in this experimentation for Riyadh station.</p

Estimation errors (RMSE) during testing for Hafr Al-Batin city using fuzzy c-means and simulated annealing algorithms.

<p>Estimation errors (RMSE) during testing for Hafr Al-Batin city using fuzzy c-means and simulated annealing algorithms.</p

Estimation errors (RMSE) during testing for Uyaynah city using fuzzy c-means and simulated annealing algorithms.

<p>Estimation errors (RMSE) during testing for Uyaynah city using fuzzy c-means and simulated annealing algorithms.</p