José Franco Machado do Amaral

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.

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.

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.

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.

Evolutionary analog circuit design on a programmable analog multiplexer array

This work discusses an Evolvable Hardware (EHW) platform for the synthesis of analog electronic circuits. The EHW analog platform, named PAMA (Programmable Analog Multiplexer Array), 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 is classified as Field Programmable Analog Array (FPAA). FPAAs have just recently appeared, and most projects are being carried out in universities and research centers. They 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. The PAMA platform architectural details, concepts and characteristics are discussed. Three case studies, with promising results, are described: an operational amplifier, a logarithmic amplifier and a membership function circuit of a fuzzy logic controller.

Evolutionary fuzzy system design and implementation

This work proposes a methodology for the design of fuzzy systems based on evolutionary computation techniques. A three-stage evolutionary algorithm that uses genetic algorithms evolves the knowledge base of a fuzzy system - rule base and parameters. The evolutionary aspect makes the design more simple and efficient, especially when compared with traditional trial and error methods. The method emphasizes interpretability so that the resulting strategy is clearly stated. An evolvable hardware platform for the synthesis of analog electronic circuits is proposed. This platform, which can be used for the implementation of the designed fuzzy system, is based on a field programmable analog array. The performance of a fuzzy system in the control of both a linear and nonlinear plant is evaluated. The results obtained with these two plants show the applicability of this hybrid model in the design of fuzzy control systems.

Automatic Synthesis of Microcontroller Assembly Code Through Linear Genetic Programming

This chapter considers the application of linear genetic programming in the automatic synthesis of microcontroller assembly language programs that implement strategies for time-optimal or sub-optimal control of the system to be controlled, based on mathematical modeling through dynamic equations. One of the difficulties presented by the conventional design of optimal control systems lies in the fact that solutions to problems of this type normally involve a highly non-linear function of the system’s state variables. As a result, it is often not possible to find an exact mathematical solution. As for the implementation of the controller, there arises the difficulty of programming the microcontroller manually in order to execute the desired control. The research that has been done in the area of automatic synthesis of assembly language programs for microcontrollers through genetic programming is surveyed in this chapter and a novel methodology in which assembly language programs are automatically synthesized, based on mathematical modeling through dynamic plant equations, is introduced. The methodology is evaluated in two case studies: the cart-centering problem and the inverted pendulum problem. The control performance of the synthesized programs is compared with that of the systems obtained by means of a tree-based genetic programming method. The synthesized programs proved to perform at least as well, but they had D. M. Dias et al.: Automatic Synthesis of Microcontroller Assembly Code Through Linear

Adaptive intelligent systems applied to two-wheeled robot

This work discuss two different intelligent controllers: Online Neuro Fuzzy Controller (ONFC) and Proportional-Integral-Derivative Neural Network (PID-NN). They were applied to maintain the equilibrium and to control the position of a two-wheeled robot prototype. Experiments were carried out to investigate the equilibrium control of the two-wheeled robot on a flat terrain and to observe the intrinsic performance in the lack of external disturbances. The effectiveness of each controller was verified by experimental results, and the performance was compared with conventional PID control scheme applied for the prototype.