Marco Aurélio Cavalcanti Pacheco

VLSI architectures for neural networks

An introduction to neural networks and neural information processing is provided. Neurocomputers are discussed, focusing on how their design exploits the architectural properties of VLSI circuits. General-purpose and special-purpose neurocomputer developments throughout the world are examined. As illustration, and to put European developments in perspective, some of the important projects in the United States and Japan are described. European research is then discussed in greater detail.<<ETX>>

Inverted hierarchical neuro-fuzzy BSP system: a novel neuro-fuzzy model for pattern classification and rule extraction in databases

This paper introduces the Inverted Hierarchical Neuro-Fuzzy BSP System (HNFB/sup -1/), a new neuro-fuzzy model that has been specifically created for record classification and rule extraction in databases. The HNFB/sup -1/ is based on the Hierarchical Neuro-Fuzzy Binary Space Partitioning Model (HNFB), which embodies a recursive partitioning of the input space, is able to automatically generate its own structure, and allows a greater number of inputs. The new HNFB/sup -1/ allows the extraction of knowledge in the form of interpretable fuzzy rules expressed by the following: If x is A and y is B, then input pattern belongs to class Z. For the process of rule extraction in the HNFB/sup -1/ model, two fuzzy evaluation measures were defined: 1) fuzzy accuracy and 2) fuzzy coverage. The HNFB/sup -1/ has been evaluated with different benchmark databases for the classification task: Iris Dataset, Wine Data, Pima Indians Diabetes Database, Bupa Liver Disorders, and Heart Disease. When compared with several other pattern classification models and algorithms, the HNFB/sup -1/ model has shown similar or better classification performance. Nevertheless, its performance in terms of processing time is remarkable. The HNFB/sup -1/ converged in less than one minute for all the databases described in the case study.

Quantum-Inspired Evolutionary Algorithm for Numerical Optimization

Since they were proposed as an optimization method, evolutionary algorithms (EA) have been used to solve problems in several research fields. This success is due, besides other things, to the fact that these algorithms do not require previous considerations regarding the problem to be optimized and offers a high degree of parallelism. However, some problems are computationally intensive regarding solutions evaluation, which makes the optimization by EAs slow for some situations. This paper proposes a novel EA for numerical optimization inspired by the multiple universes principle of quantum computing. Results show that this algorithm can find better solutions, with less evaluations, when compared with similar algorithms.

Well Placement Optimization Using a Genetic Algorithm With Nonlinear Constraints

Well placement optimization is a very challenging problem due to the large number of decision variables involved and the nonlinearity of the reservoir response as well as of the well placement constraints. Over the years, a lot of research has been done on this problem, most of which using optimization routines coupled to reservoir simulation models. Despite all this research, there is still a lack of robust computer-aided optimization tools ready to be applied by asset teams in real field development projects. This paper describes the implementation of a tool, based on a Genetic Algorithm, for the simultaneous optimization of number, location and trajectory of producer and injector wells. The developed software is the result of a two-year project focused on a robust implementation of a computer-aided optimization tool to deal with realistic well placement problems with arbitrary well trajectories, complex model grids and linear and nonlinear constraints. The developed optimization tool uses a commercial reservoir simulator as the evaluation function without using proxies to substitute the full numerical model. Due to the large size of the problem, in some cases involving more than 100 decision variables, the optimization process may require thousands of reservoir simulations. Such a task has become feasible through a distributed computing environment running multiple simulations at the same time. The implementation uses a technique called Genocop III – Genetic Algorithm for Numerical Optimization of Constrained Problems – to deal with well placement constraints. Such constraints include grid size, maximum length of wells, minimum distance between wells, inactive grid cells and user-defined regions of the model, with non-uniform shape, where the optimization routine is not supposed to place wells. The optimization process was applied to three full-field reservoir models based on real cases. It increased the net present values and the oil recovery factors obtained by well placement scenarios previously proposed by reservoir engineers. The process was also applied to a synthetic case, based on outcrop data, to analyze the impact of using reservoir quality maps to generate an initial well placement scenario for the optimization routine without using an engineer-defined configuration. Introduction The definition of a well placement is a key aspect with major impact in a field development project. In this sense, the use of reservoir simulation allows the engineer to evaluate different placement scenarios. However, the current industry practice is still, in most cases, a manual procedure of trial and error that requires a lot of experience and knowledge from the engineers involved in the project. Considering that, the development of well placement optimization tools which can automate this process is a high desirable goal. Well placement optimization is a very challenging problem due to the large number of decision variables involved and the nonlinearity of the reservoir response as well as of the well placement constraints. Over the years, a lot of research has been done on this problem, most of which using optimization routines coupled to reservoir simulation and economical models. In 1995, Beckner and Song applied a Simulated Annealing algorithm to optimize the location and scheduling of 12 wells with fixed orientation and length. In 1997, Bittencourt and Horner applied a Genetic Algorithm (GA) hybridized with Polytope and Tabu Search methods to optimize the location of 33 vertical and horizontal wells, including wells, producers and injectors. In 1998, Pan and Horner investigated the use of multivariate interpolation algorithms, Least Squares and Kriging, as proxies to reservoir simulations for optimization problems including well placement. In 1999, Cruz et al. introduced the

Comparison of different evolutionary methodologies applied to electronic filter design

We present in this work the application of a set of different evolutionary methodologies in the problem of electronic filter design. The main objectives are to find out which constraints in the filter topologies, if any, must be observed along the evolutionary process and to study the problem of convergence to parsimonious circuits. The new area of evolutionary electronics is introduced, an evolutionary methodology based on variable length representation is presented and the results on the evolution of low-pass and band-pass filters are described.

Hierarchical neuro-fuzzy quadtree models

Hybrid neuro-fuzzy systems have been in evidence during the past few years, due to its attractive combination of the learning capacity of artificial neural networks with the interpretability of the fuzzy systems. This article proposes a new hybrid neuro-fuzzy model, named hierarchical neuro-fuzzy quadtree (HNFQ), which is based on a recursive partitioning method of the input space named quadtree. The article describes the architecture of this new model, presenting its basic cell and its learning algorithm. The HNFQ system is evaluated in three well known benchmark applications: the sinc(x, y) function approximation, the Mackey Glass chaotic series forecast and the two spirals problem. When compared to other neurofuzzy systems, the HNFQ exhibits competing results, with two major advantages it automatically creates its own structure and it is not limited to few input variables.

Analog Circuits Evolution in Extrinsic and Intrinsic Modes

Our work focuses on the use of artificial evolution in Computer Aided Design (CAD) of electronic circuits. Artificial evolution promises to be an important tool for analog CAD development, due to the nature of this task, which has been proven to be much less amenable for standard tools than its digital counterparts. Analog design relies more on the designer’s experience than on systematic rules or procedures. The recent appearance of Field Programmable Analog Arrays (FPAAs) allows evolution to be performed in real silicon, which opens new possibilities to the field. Our work addresses the evolution of amplifiers and oscillators, through the use of a standard simulator and a programmable analog circuit respectively. Furthermore, the issue of the implementability of the circuits evolved in simulation is also examined.

Artificial evolution of active filters: a case study

This article focuses on the application of artificial avolution to the synthesis of analog active filters. The main objective of this research is the achievement of a new class of systems, with advantageous features compared to conventional ones, such as lower power consumption, higher speed and more robustness to noise. The particular problem of designing the amplifier of an AM receiver is examined in this work. Genetic algorithms are employed as our evolutionary tool and two sets of experiments are described. The first set has been carried out using a single objective, the desired frequency response of the circuit. In a second set of experiments, three other objectives have been included in the system. A new multi-objective evaluation methodology was conceived for this second set of experiments. A second approach for evolving active filters, using programmable chips, is also discussed in this paper.

Variable Length Representation in Evolutionary Electronics

This work investigates the application of variable length representation (VLR) evolutionary algorithms (EAs) in the field of Evolutionary Electronics. We propose a number of VLR methodologies that can cope with the main issues of variable length evolutionary systems. These issues include the search for efficient ways of sampling a genome space with varying dimensionalities, the task of balancing accuracy and parsimony of the solutions, and the manipulation of non-coding segments. We compare the performance of three proposed VLR approaches to sample the genome space: Increasing Length Genotypes, Oscillating Length Genotypes, and Uniformly Distributed Initial Population strategies. The advantages of reusing genetic material to replace non-coding segments are also emphasized in this work. It is shown, through examples in both analog and digital electronics, that the variable length genotypes representation is natural to this particular domain of application. A brief discussion on biological genome evolution is also provided.

Evolvable Systems in Hardware Design: Taxonomy, Survey and Applications

This article proposes a taxonomy, presents a survey and describes a set of applications on Evolvable Hardware Systems (EHW). The taxonomy is based on the following properties: Hardware Evaluation Process, Evolutionary Approach, Target Application Area and Evolving Platform. Recent reported applications on EHW are also reviewed, according to the proposed taxonomy. Additionally, a set of digital design applications, developed by the authors are presented. The applications consist in evolving basic digital devices, and the main objective is to evaluate the performance of an EHW system in terms of chromosome representation and evaluation.