There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

In this paper, a novel design method for determining the optimal proportional-integral-derivative (PID) controller parameters of an AVR system using the particle swarm optimization (PSO) algorithm is presented. This paper demonstrated in detail how to employ the PSO method to search efficiently the optimal PID controller parameters of an AVR system. The proposed approach had superior features, including easy implementation, stable convergence characteristic, and good computational efficiency. Fast tuning of optimum PID controller parameters yields high-quality solution. In order to assist estimating the performance of the proposed PSO-PID controller, a new time-domain performance criterion function was also defined. Compared with the genetic algorithm (GA), the proposed method was indeed more efficient and robust in improving the step response of an AVR system.

A particle swarm optimization approach for optimum design of PID controller in AVR system

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

/pdf/a-survey-on-analysis-and-design-of-model-based-fuzzy-control-3hrjlrel0k.pdf

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

A dissimilarity measure estimation for analyzing trajectory data

Understanding the information and clusters hidden inside multidimensional data can be challenging and complicated. Dimension reduction is usually considered as the first step for data analysis and interpretation. The focus of this paper is on the improvement of data clustering performance of Self Organising Maps (SOM) by embedding Auto-Associative Neural Networks (AANN). SOM is known as a computational tool that carries out topology preservation from high-dimensional input space onto a low-dimensional grid such as two-dimensional (2D) map. It has been used to visualize and explore inherent clusters and properties of the data. In this paper, a structurally flexible combination of AANN and SOM is developed, applied and investigated on Iris Flowers and Italian Olive oils datasets. The results have shown that the combined technique of AANNSOM has led to improvement of data clustering performance. It has reduced quantization error by 93.1%, and topographic error by 35.2%, when compared to SOM alone.

Hybrid neural networks for dimension reduction and clustering of multidimensional data

A technique for the modelling of nonlinear process control using recursive least square and Takagi-Sugeno fuzzy system with genetic algorithm topology is described. This paper discusses the identification of parameters of the fuzzy sets at the antecedent part and linear model at the consequent part of fuzzy model within an application to process control. The key issues of finding the best model of the process are described. Results show that fuzzy model with genetic algorithm gives minimum mean squared error compare with recursive least square

/pdf/recursive-least-square-and-fuzzy-modelling-using-genetic-1jmxs064iy.pdf

Recursive Least Square and Fuzzy Modelling Using Genetic Algorithm for Process Control Application

This paper describes and shows an H ∞ controller design based on the Butterworth filter conversion. The proposed method refers to the conception of a normalized plant. By converting the control target to a Butterworth filter, it is not necessary to redesign the controller depending on the control target. Simulation and experimental results show the proposed method to be robust against an external disturbance and a modeling error.

An H ∞ controller design based on the butterworth filter conversion

This paper presents the modified model of a full dynamic mathematical model of HVAC components for the first time. Because of nonlinearity and the interaction of the system it is considered to simplify the model of the system. Therefore System Identification Toolbox is used to achieve this goal. After getting of whole transfer functions of the MIMO system the decoupling method is used to convert the MIMO modified system to single SISO systems. Relative Gain Array is used for decoupling method One of the advantages of this method is that the interaction of the system is reduced and controller can be controlled the separate model without any interactions. Finally, different types of tuning PID controller are used to control the parameters of the HVAC system and compare with the original one to show how much the simplified model could be get the target.

/pdf/comparison-of-control-s-parameters-of-simplified-full-3g70pzpgw9.pdf

Comparison of Control's Parameters of Simplified Full Mathematical Dynamic Model of HVAC System

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