Computational methods in Bioinformatics: Introduction, Review, and Challenges

Biotechnology is emerging as a new driving force for the global economy in the 21st century. An important engine for biotechnology is Bioinformatics. Bioinformatics has revolutionized biology research and drug discovery. Bioinformatics is an amalgamation of biological sciences, computer science, applied math, and systems science. The report provides a brief introduction to molecular biology for non-biologists, with focus on understanding the basic biological problems which triggered the exponentially growing research activities in the bioinformatics fields. The report provides as well a comprehensive literature review of the main challenging problems, and the current tools and algorithms. In particular, the problems of gene modeling, and gene prediction, similarity search, multiple alignments of proteins, and the protein folding problems are highlighted. The report discusses as well how such tools as dynamic programming, hidden Markov models, statistical analysis, clustering, decision trees, fuzzy theory, and neural networks have been applied in solving these problems

Computational methods in Bioinformatics: Introduction, Review, and Challenges

Biotechnology is emerging as a new driving force for the global economy in the 21st century. An important engine for biotechnology is Bioinformatics. Bioinformatics has revolutionized biology research and drug discovery. Bioinformatics is an amalgamation of biological sciences, computer science, applied math, and systems science. The report provides a brief introduction to molecular biology for non-biologists, with focus on understanding the basic biological problems which triggered the exponentially growing research activities in the bioinformatics fields. The report provides as well a comprehensive literature review of the main challenging problems, and the current tools and algorithms. In particular, the problems of gene modeling, and gene prediction, similarity search, multiple alignments of proteins, and the protein folding problems are highlighted. The report discusses as well how such tools as dynamic programming, hidden Markov models, statistical analysis, clustering, decision trees, fuzzy theory, and neural networks have been applied in solving these problems

Optimal Fuzzy Regulator

The conventional design of fuzzy logic controller uses human intuition and experience, which rarely constitutes an optimal design. In this paper, a systematic design procedure is presented to realize the optimal fuzzy logic regulator. The proposed scheme minimizes a suitable criterion function employing the steepest descent method. The recently proposed Block Partial Derivative (BPD) is used to facilitate the gradient computation

Design Of A Fuzzy Servo-Controller

A design method of a fuzzy servo-controller for nonlinear plants has been presented.The proposed method is an error feedback scheme, where the controller also receives signals representing the plant operating points.Integrator is used in the control loop to ensure setpoint following, low-frequency disturbance rejection, and to enhance the robustness of the closed-loop system. A training scheme for the fuzzy controller is derived that minimizes the output error between a reference model and the plant. The training is conducted off-line for a class of setpoints conforming to the normal operating condition of the plant. Results of simulation studies are also presented. (C) 2001 Elsevier Science BN. All rights reserved

Optimal Fuzzy Regulator

The conventional design of fuzzy logic controller uses human intuition and experience, which rarely constitutes an optimal design. In this paper, a systematic design procedure is presented to realize the optimal fuzzy logic regulator. The proposed scheme minimizes a suitable criterion function employing the steepest descent method. The recently proposed Block Partial Derivative (BPD) is used to facilitate the gradient computation

Design Of A Fuzzy Servo-Controller

A design method of a fuzzy servo-controller for nonlinear plants has been presented. The proposed method is an error feedback scheme, where the controller also receives signals representing the plant operating points. Integrator is used in the control loop to ensure setpoint following, low-frequency disturbance rejection, and to enhance the robustness of the closed-loop system. A training scheme for the fuzzy controller is derived that minimizes the output error between a reference model and the plant. The training is conducted off-line for a class of setpoints conforming to the normal operating condition of the plant. Results of simulation studies are also presented. (C) 2001 Elsevier Science BN. All rights reserved

Design Of A Fuzzy Servo-Controller

A design method of a fuzzy servo-controller for nonlinear plants has been presented.The proposed method is an error feedback scheme, where the controller also receives signals representing the plant operating points.Integrator is used in the control loop to ensure setpoint following, low-frequency disturbance rejection, and to enhance the robustness of the closed-loop system. A training scheme for the fuzzy controller is derived that minimizes the output error between a reference model and the plant. The training is conducted off-line for a class of setpoints conforming to the normal operating condition of the plant. Results of simulation studies are also presented. (C) 2001 Elsevier Science BN. All rights reserved