Cleber Zanchettin

An Optimization Methodology for Neural Network Weights and Architectures

This paper introduces a methodology for neural network global optimization. The aim is the simultaneous optimization of multilayer perceptron (MLP) network weights and architectures, in order to generate topologies with few connections and high classification performance for any data sets. The approach combines the advantages of simulated annealing, tabu search and the backpropagation training algorithm in order to generate an automatic process for producing networks with high classification performance and low complexity. Experimental results obtained with four classification problems and one prediction problem has shown to be better than those obtained by the most commonly used optimization techniques

Hybrid Training Method for MLP: Optimization of Architecture and Training

The performance of an artificial neural network (ANN) depends upon the selection of proper connection weights, network architecture, and cost function during network training. This paper presents a hybrid approach (GaTSa) to optimize the performance of the ANN in terms of architecture and weights. GaTSa is an extension of a previous method (TSa) proposed by the authors. GaTSa is based on the integration of the heuristic simulated annealing (SA), tabu search (TS), genetic algorithms (GA), and backpropagation, whereas TSa does not use GA. The main advantages of GaTSa are the following: a constructive process to add new nodes in the architecture based on GA, the ability to escape from local minima with uphill moves (SA feature), and faster convergence by the evaluation of a set of solutions (TS feature). The performance of GaTSa is investigated through an empirical evaluation of 11 public-domain data sets using different cost functions in the simultaneous optimization of the multilayer perceptron ANN architecture and weights. Experiments demonstrated that GaTSa can also be used for relevant feature selection. GaTSa presented statistically relevant results in comparison with other global and local optimization techniques.

A KNN-SVM hybrid model for cursive handwriting recognition

This paper presents a hybrid KNN-SVM method for cursive character recognition. Specialized Support Vector Machines (SVMs) are introduced to significantly improve the performance of KNN in handwrite recognition. This hybrid approach is based on the observation that when using KNN in the task of handwritten characters recognition, the correct class is almost always one of the two nearest neighbors of the KNN. Specialized local SVMs are introduced to detect the correct class among these two different classification hypotheses. The hybrid KNN-SVM recognizer showed significant improvement in terms of recognition rate compared with MLP, KNN and a hybrid MLP-SVM approach for a task of character recognition.

A Heuristic Binarization Algorithm for Documents with Complex Background

This paper proposes a new method for binarization of digital documents. The proposed approach performs binarization by using a heuristic algorithm with two different thresholds and the combination of the thresholded images. The method is suitable for binarization of complex background document images. In experiments, it obtained better results than classical techniques in the binarization of real bank checks.

A SVM based off-line handwritten digit recognizer

This paper presents an efficient method for handwritten digit recognition. The proposed method makes use of Support Vector Machines (SVM), benefitting from its generalization power. The method presents improved recognition rates when compared to Multi-Layer Perceptron (MLP) classifiers, other SVM classifiers and hybrid classifiers. Experiments and comparisons were done using a digit set extracted from the NIST SD19 digit database. The proposed SVM method achieved higher recognition rates and it outperformed other methods. It is also shown that although using solely SVMs for the task, the new method does not suffer when considering processing time.

DESIGN OF EXPERIMENTS IN NEURO-FUZZY SYSTEMS

Interest in hybrid methods that combine artificial neural networks and fuzzy inference systems has grown in recent years. These systems are robust solutions that search for representations of domain knowledge, reasoning on uncertainty, automatic learning and adaptation. However, the design and definition of the parameter effectiveness of such systems is still a hard task. In the present work, we perform a statistical analysis to verify interactions and interrelations between parameters in the design of neuro-fuzzy systems. The analysis is carried out using a powerful statistical tool, namely, Design of Experiments (DOE), in two neuro-fuzzy models — Adaptive Neuro Fuzzy Inference System (ANFIS) and Evolving Fuzzy Neural Networks (EFuNN). The results show that, for ANFIS, input MFs number and output MFs shape are usually the factors with the largest influence on the systems RMSE. For EFFuNN, the MF shape and the interaction between MF shape and number usually have the largest effect size.

Hybrid neural systems for pattern recognition in artificial noses.

This work examines the use of Hybrid Intelligent Systems in the pattern recognition system of an artificial nose. The connectionist approaches Multi-Layer Perceptron and Time Delay Neural Networks, and the hybrid approaches Feature-Weighted Detector and Evolving Neural Fuzzy Networks were investigated. A Wavelet Filter is evaluated as a preprocessing method for odor signals. The signals generated by an artificial nose were composed by an array of conducting polymer sensors and exposed to two different odor databases.

Design of experiments in neuro-fuzzy systems

Interest in hybrid methods that combine artificial neural networks and fuzzy inference systems has grown. These systems are robust solutions that search for representation of domain knowledge, reasoning on uncertainty, automatic learning and adaptation. However, the design and the definition of parameters effectiveness of these systems is a hard task yet. In this paper we perform a statistical analysis to verify the interactions and interrelations between parameters in the design of neuro-fuzzy systems. The analysis carries out using a powerful statistical tool, the design of experiments (DOE) in two neuro-fuzzy models, adaptive neuro fuzzy inference system (ANFIS) and evolving fuzzy neural networks (EFuNN).

Hybrid technique for artificial neural network architecture and weight optimization

This work presents a technique that integrates the heuristics tabu search, simulated annealing, genetic algorithms and backpropagation. This approach obtained promising results in the simultaneous optimization of the artificial neural network architecture and weights.

Sistemas neurais híbridos para reconhecimento de padrões em narizes artificiais

The present invention relates to a system and method for providing redundancy in a hierarchically memory, by replacing small blocks in such memory. The present invention provides such redundancy (i.e., replaces such small blocks) by either shifting predecoded lines or using a modified shifting predecoder circuit in the local predecoder block. In one embodiment, the hierarchal memory structure includes at least one active predecoder adapted to be shifted out of use; and at least one redundant predecoder adapted to be shifted in to use.