This paper investigates the application of the mutual information criterion to evaluate a set of candidate features and to select an informative subset to be used as input data for a neural network classifier. Because the mutual information measures arbitrary dependencies between random variables, it is suitable for assessing the "information content" of features in complex classification tasks, where methods bases on linear relations (like the correlation) are prone to mistakes. The fact that the mutual information is independent of the coordinates chosen permits a robust estimation. Nonetheless, the use of the mutual information for tasks characterized by high input dimensionality requires suitable approximations because of the prohibitive demands on computation and samples. An algorithm is proposed that is based on a "greedy" selection of the features and that takes both the mutual information with respect to the output class and with respect to the already-selected features into account. Finally the results of a series of experiments are discussed. >

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Using mutual information for selecting features in supervised neural net learning

Fuzzy logic control was originally introduced and developed as a model free control design approach. However, it unfortunately suffers from criticism of lacking of systematic stability analysis and controller design though it has a great success in industry applications. In the past ten years or so, prevailing research efforts on fuzzy logic control have been devoted to model-based fuzzy control systems that guarantee not only stability but also performance of closed-loop fuzzy control systems. This paper presents a survey on recent developments (or state of the art) of analysis and design of model based fuzzy control systems. Attention will be focused on stability analysis and controller design based on the so-called Takagi-Sugeno fuzzy models or fuzzy dynamic models. Perspectives of model based fuzzy control in future are also discussed

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A Survey on Analysis and Design of Model-Based Fuzzy Control Systems

A Multi-Parametric Toolbox (MPT) for computing optimal or suboptimal feedback controllers for constrained linear and piecewise affine systems is under development at ETH The toolbox offers a broad spectrum of algorithms compiled in a user friendly and accessible format: starting from different performance objectives (linear, quadratic, minimum time) to the handling of systems with persistent additive disturbances and polytopic uncertainties The algorithms included in the toolbox are a collection of results from recent publications in the field of constrained optimal control of linear and piecewise affine systems [10,13,4,9,16,17,15,14,7]

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Multi-Parametric Toolbox (MPT)

Neural networks and fuzzy systems

Electrical load forecasting plays a vital role in order to achieve the concept of next generation power system such as smart grid, efficient energy management and better power system planning. As a result, high forecast accuracy is required for multiple time horizons that are associated with regulation, dispatching, scheduling and unit commitment of power grid. Artificial Intelligence (AI) based techniques are being developed and deployed worldwide in on Varity of applications, because of its superior capability to handle the complex input and output relationship. This paper provides the comprehensive and systematic literature review of Artificial Intelligence based short term load forecasting techniques. The major objective of this study is to review, identify, evaluate and analyze the performance of Artificial Intelligence (AI) based load forecast models and research gaps. The accuracy of ANN based forecast model is found to be dependent on number of parameters such as forecast model architecture, input combination, activation functions and training algorithm of the network and other exogenous variables affecting on forecast model inputs. Published literature presented in this paper show the potential of AI techniques for effective load forecasting in order to achieve the concept of smart grid and buildings.

A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings

This paper describes the design principle, tracking performance, and stability analysis of a fuzzy proportional-derivative (PD) controller. First, the fuzzy PD controller is derived from the conventional continuous-time linear PD controller. Then, the fuzzification, control-rule base, and defuzzification in the design of the fuzzy PD controller are discussed in detail. The resulting controller is a discrete-time fuzzy version of the conventional PD controller, which has the same linear structure in the proportional and the derivative parts but has nonconstant gains: both the proportional and derivative gains are nonlinear functions of the input signals. The new fuzzy PD controller thus preserves the simple linear structure of the conventional PD controller yet enhances its self-tuning control capability. Computer simulation results have demonstrated this advantage of the fuzzy PD controller, particularly when the process to be controlled is nonlinear. After a detailed stability analysis, where a simple and realistic sufficient condition for the bounded-input/bounded-output stability of the overall feedback control system was derived, several computer simulation results are compared with the conventional PD controller. Although the conventional and fuzzy PD controllers are not exactly comparable, the authors compare them in order to have a sense of how well the fuzzy PD controller performs. For this reason, in the simulations several first-order and second-order linear systems, with or without time-delays, are first used to test the performance of the fuzzy PD controller for step reference inputs: the fuzzy PD control systems show remarkable performance, as well as (if not better than) the conventional PD control systems. Moreover, the fuzzy PD controller is compared to the conventional PD controller for a particular second-order linear system, showing the advantage of the fuzzy PD controller over the conventional one in the sense that in order to obtain the same control performance the conventional PD controller has to employ an extremely large gain while the fuzzy controller uses a reasonably small gain. Finally, in the case of nonlinear systems, the authors provide some examples to show that the fuzzy PD controller can track the set-points satisfactorily but the conventional PD controller cannot. >

New design and stability analysis of fuzzy proportional-derivative control systems

Design and analysis of a fuzzy proportional-integral-derivative controller

This paper presents the design and experiment of a fuzzy proportional integral derivative (PID) controller for a flexible-joint robot arm with uncertainties from time-varying loads. Experimental results have shown remarkable tracking performance of this fuzzy PID controller, and have convincingly demonstrated that fuzzy logic control can be used for flexible-joint robot arms with uncertainties and it is quite robust. In this paper, the fuzzy PID controller is first described briefly, using a simple and practical PD+I controller configuration. This configuration preserves the linear structure of the conventional PD+I controller, but has nonconstant gains: the proportional, integral, and derivative gains are nonlinear functions of their input signals, which have self-tuning (adaptive) capabilities in set-point tracking performance. Moreover, these variable gains make the fuzzy PID controller robust with faster response time and less overshoot than its conventional counterpart. The proposed design was tested using a flexible-joint robot arm driven by a DC motor in a laboratory, where the arm was experienced with time-varying loads. Control performance by the conventional and fuzzy PID controllers for such a laboratory robotic system are both included in this paper for comparison.

Fuzzy PID control of a flexible-joint robot arm with uncertainties from time-varying loads

From the Publisher:
This book presents All of the major topics in modern analog and digital control systems, along with the practical, applications oriented knowledge and skills needed by technicians. It contains user-friendly conceptual explanations and clearly written mathematical developments. Examples of both Mathcad and MATLAB illustrate computer problem solvingbut this book emphasizes the ability to use any suitable software to achieve successful results in solving problems and performing design. Chapter topics include Measurement; Laplace Transforms; Control System Models; Static and Dynamic Response; Stability; Frequency Response Analysis; Root Locus; State Variable Analysis; Introduction to Discrete Control Systems; Z-Transforms and Discrete State-Space Analysis; Digital Signal Representations; Discrete Time Control Systems; Stability of Discrete Control Systems; and Advanced Topics in Control Systems. For engineers and technicians working for companies that integrate control systems with the use of programmable logic controllers.

Control Systems Technology

This paper presents a neural network-based intrusion detection method for the internet-based attacks on a computer network. Intrusion detection systems (IDS) have been created to predict and thwart current and future attacks. Neural networks are used to identify and predict unusual activities in the system. In particular, feedforward neural networks with the back propagation training algorithm were employed in this study. Training and testing data were obtained from the Defense Advanced Research Projects Agency (DARPA) intrusion detection evaluation data sets. The experimental results on real-data showed promising results on detection intrusion systems using neural networks.

Heidar A. Malki

Papers

New design and stability analysis of fuzzy proportional-derivative control systems

Design and analysis of a fuzzy proportional-integral-derivative controller

Fuzzy PID control of a flexible-joint robot arm with uncertainties from time-varying loads

Control Systems Technology

Network Intrusion Detection System Using Neural Networks