Ricardo Tanscheit

Maximum-Likelihood Data Fusion of Phase-Difference and Threshold-Detection Techniques for Wind-Speed Measurement

Analyses of threshold-detection and phase-difference techniques for wind-speed measurement using ultrasonic transducers are presented. The influence of uncertainties that are associated with additive noise and attenuation of the ultrasonic signal on the wind-speed measurement uncertainty is analyzed. A data-fusion procedure based on the maximum-likelihood estimation (MLE) algorithm is developed for the determination of wind speed, with data gathered through threshold-detection and phase-difference techniques. The data-fusion procedure provides a lower measurement uncertainty than those obtained with the above techniques when taken separately. Practical design issues are considered, and an application example is shown to illustrate the proposed procedure.

An immune inspired fault diagnosis system for analog circuits using wavelet signatures

This work focuses on fault diagnosis of electronic analog circuits. A fault diagnosis system for analog circuits based on wavelet decomposition and artificial immune systems is proposed. It is capable of detecting and identifying faulty components in analog circuits by analyzing its impulse response. The use of wavelet decomposition for preprocessing of the impulse response drastically reduces the size of the detector used by the Real-valued Negative Selection Algorithm (RNSA). Results have demonstrated that the proposed system is able to detect and identify faults in a Sallen-Key bandpass filter circuit.

GPFIS-CONTROL: A GENETIC FUZZY SYSTEM FOR CONTROL TASKS

Abstract This work presents a Genetic Fuzzy Controller (GFC), called Genetic Programming Fuzzy Inference System for Control tasks (GPFIS-Control). It is based on Multi-Gene Genetic Programming, a variant of canonical Genetic Programming. The main characteristics and concepts of this approach are described, as well as its distinctions from other GFCs. Two benchmarks application of GPFIS-Control are considered: the Cart-Centering Problem and the Inverted Pendulum. In both cases results demonstrate the superiority and potentialities of GPFIS-Control in relation to other GFCs found in the literature.

An Immune Fault Detection System for Analog Circuits with Automatic Detector Generation

This work focuses on fault detection of electronic analog circuits. A fault detection system for analog circuits based on cross-correlation and artificial immune system is proposed. It is capable of detecting faulty components in analog circuits by analyzing its impulse response. The use of cross-correlation for preprocessing the impulse response drastically reduces the size of the detector used by the real-valued negative selection algorithm (RNSA). The proposed method can automatically generate very efficient detectors by using quadtree decomposition. Results have demonstrated that the proposed system is able to detect faults in a Sallen-Key bandpass filter and in a continuous-time state variable filter.

Fuzzy Rules Extraction from Support Vector Machines for Multi-class Classification

This paper proposes a new method for fuzzy rule extraction from trained support vector machines (SVMs) for multi-class problems, named FREx_SVM. SVMs have been used in a variety of applications. However, they are considered “black box models,” where no interpretation about the input–output mapping is provided. Some methods to reduce this limitation have already been proposed, but they are restricted to binary classification problems and to the extraction of symbolic rules with intervals or functions in their antecedents. In order to improve the interpretability of the generated rules, this paper presents a new model for extracting fuzzy rules from a trained SVM. The proposed model is suited for classification in multi-class problems and includes a wrapper feature selection algorithm. It is evaluated in four benchmark databases, and results obtained demonstrate its capacity to generate a reduced set of interpretable fuzzy rules that explains both the classification database and the influence of each input variable on the determination of the final class.

Towards evolvable analog artificial neural networks controllers

This work deals with the design of analog circuits for artificial neural networks (ANNs) controllers using an evolvable hardware (EHW) platform. ANNs are massively parallel systems that rely on simple processors and dense arrangements of interconnections. These networks have demonstrated their ability to deliver simple and powerful solutions in several areas, including control systems. The EHW analog platform is a reconfigurable platform, called programmable analog multiplexer array-next generation (PAMA-NG), which can be programmed by genetic algorithms to synthesize circuits. This article focuses on the development of artificial neuron circuits for analog ANNs on the PAMA-NG.

Hierarchical type-2 neuro-fuzzy BSP model

This paper presents a novel hybrid interval type-2 neuro-fuzzy inference system, with automatic learning of all its parameters, to handle uncertainty. This new model, called hierarchical type-2 neuro-fuzzy BSP model (T2-HNFB), combines the paradigms of the type-2 fuzzy inference systems and neural networks with recursive partitioning techniques (binary space partitioning - BSP). The model is able to automatically create and expand its own structure, to reduce limitations on the number of inputs and to extract fuzzy linguistic rules from a dataset, as well as to efficiently model and manipulate most types of uncertainty existing in real situations. In addition, it provides an interval for its output, which can be regarded as a measure of uncertainty and constitutes important information for real applications. In this context, this model overcomes the limitations of the conventional type-2 and type-1 fuzzy inference systems. Experimental results show that the results provided by the T2-HNFB model are close to and in several cases better than the best results supplied by the other models used for comparison.

Real-valued negative selection algorithm with a Quasi-Monte Carlo genetic detector generation

A new scheme for detector generation for the Real-Valued Negative Selection Algorithm (RNSA) is presented. The proposed method makes use of genetic algorithms and Quasi-Monte Carlo Integration to automatically generate a small number of very efficient detectors. Results have demonstrated that a fault detection system with detectors generated by the proposed scheme is able to detect faults in analog circuits and in a ball bearing dataset.

Evolvable building blocks for analog fuzzy logic controllers

This work discusses the use of an evolvable hardware (EHW) platform in the synthesis of analog electronic circuits for fuzzy logic controllers. A fuzzy logic controller (FLC) is defined by a collection of fuzzy if-then rules and a set of membership functions characterizing the linguistic terms associated with the inputs and output of the FLC. The EHW analog platform, named PAMA-NG (programmable analog multiplexer array - next generation), is a reconfigurable platform that consists of integrated circuits whose internal connections can be programmed by evolutionary computation techniques, such as genetic algorithms, to synthesize circuits. The PAMA-NG is classified as a field programmable analog array (FPAA). FPAAs have appeared recently and constitute the state of the art in the technology of reconfigurable platforms. These devices will become the building blocks of a forthcoming class of hardware, with the important features of self-adaptation and self-repairing, through automatic reconfiguration. This article focuses on the development of building blocks for analog FLCs on the PAMA-NG and presents case studies.

A neuro-fuzzy-genetic system for automatic setting of control strategies

The article deals with the design of control systems based on hybrid techniques of computational intelligence. Initially, a neuro-fuzzy system is employed in the control of several plants. The neuro-fuzzy system used here is the NEFCON model, which is capable of learning and optimizing online the rulebase of a Mamdani-type fuzzy controller. The algorithm is based on reinforcement learning that uses a fuzzy measure for the error. Its performances in the control of linear plants of diverse complexity and also of a nonlinear one are evaluated. Results are compared to those obtained through conventional techniques. The main focus of the work is on the development of a new neuro-fuzzy-genetic system, which makes use of genetic algorithms for rule base optimization. The satisfactory results obtained with the two more complex plants show the potential of this hybrid model in the design of control systems.