Catálogo de publicaciones - libros

In electrical impedance tomography (EIT), various image reconstruction algorithms have been used in order to compute the internal resistivity distribution of the unknown object with its electric potential data at the boundary. Mathematically the EIT image reconstruction algorithm is a nonlinear ill-posed inverse problem. This paper presents a genetic algorithm technique for the solution of the static EIT inverse problem. The computer simulation for the 32 channels synthetic data shows that the spatial resolution of reconstructed images in the proposed scheme is improved compared to that of the modified Newton–Raphson algorithm at the expense of increased computational burden.

Formation of hamming cliff hampers the progress of genetic algorithm in seemingly deceptive problems. We demonstrate through an analysis of neighbourhood search capabilities of the mutation operator in genetic algorithm that the problem can somtimes be overcome through proper genetic coding. Experiments have been conducted on a 4-bit deceptive function and the pure-integer programming problem. The integer-coded genetic algorithm performs better and requires less time than the binary-coded genetic algorithm in these problems.

In this paper, a hybrid genetic algorithm for blind signal separation that extracts the individual unknown independent source signals out of given linear signal mixture is presented. The proposed method combines a genetic algorithm with local search and is called the hybrid genetic algorithm. The implemented separation method is based on evolutionary minimization of the separated signal cross-correlation. The convergence behaviour of the network is demonstrated by presenting experimental separating signal results. A computer simulation example is given to demonstrate the effectiveness of the proposed method. The hybrid genetic algorithm blind signal separation performance is better than the genetic algorithm at directly minimizing the Kullback-Leibler divergence. Eventually, it is hopeful that this optimization approach can be helpful for blind signal separation engineers as a simple, useful and reasonable alternative.

The problem of assigning customers to satellite channels is considered. Finding an optimal allocation of customers to satellite channels is a difficult combinatorial optimization problem and is shown to be NP-complete in an earlier study. We propose a genetic algorithm (GA) approach to search for the best/optimal assignment of customers to satellite channels. Various issues related to genetic algorithms such as solution representation, selection methods, genetic operators and repair of invalid solutions are presented. A comparison of this approach with the standard optimization method is presented to show the advantages of this approach in terms of computation time.

Lot output time series is one of the most important time series data in a wafer fab (fabrication plant). Predicting the output time of every lot is therefore a critical task to the wafer fab. To further enhance the effectives and efficiency of wafer lot output time prediction, a look-ahead fuzzy back propagation network (FBPN) is constructed in this study with two advanced features: the future release plan of the fab is considered (look-ahead); expert opinions are incorporated. Production simulation is also applied in this study to generate test examples. According to experimental results, the prediction accuracy of the look-ahead FBPN was significantly better than those of four existing approaches: multiple-factor linear combination (MFLC), BPN, case-based reasoning (CBR), and FBPN without look-ahead, by achieving a 12%~37% (and an average of 19%) reduction in the root-mean-squared-error (RMSE) over the comparison basis – MFLC.

In this paper, we propose a new design methodology that adopts Information Granulation to the structure of fuzzy-neural networks called Fuzzy Set-based Polynomial Neural Networks (FSPNN). We find the optimal structure of the proposed model with the aid of symbolic genetic algorithms which has symbolic gene type chromosomes. We are able to find information related to real system with Information Granulation through numerical data. Information Granules obtained from Information Granulation help us understand real system without the field expert. In Information Granulation, we use conventional Hard C-Means Clustering algorithm and proposed procedure that handle the apex of clusters using ‘Union’ and ‘Intersection’ operation. We use genetic algorithm to find optimal structure of the proposed networks. The proposed networks are based on GMDH algorithm that makes whole networks dynamically. In other words, FSPNN is built dynamically with symbolic genetic algorithms. Symbolic gene type has better characteristic than binary coding GAs from the size of solution space’s point of view. Symbolic genetic algorithms are capable of reducing the solution space more than conventional genetic algorithms with binary genetype chromosomes. The performance of genetically optimized FSPNN (gFSPNN) with aid of symbolic genetic algorithms is quantified through experimentation where we use a number of modeling benchmarks data which are already experimented with in fuzzy or neurofuzzy modeling.

The recording of symbolic interval data has become a common practice with the recent advances in database technologies. This paper presents a fuzzy -means clustering algorithm for symbolic interval data. This method furnishes a partition of the input data and a corresponding prototype (a vector of intervals) for each class by optimizing an adequacy criterion which is based on a suitable single adaptive Euclidean distance between vectors of intervals. Experiments with real and synthetic symbolic interval data sets showed the usefulness of the proposed method.

Understanding data is usually done extracting fuzzy or crisp logical rules using neurofuzzy systems, decision trees and other approaches. Prototype-based rules are an interesting alternative providing in many cases simpler, more accurate and more comprehensible description of the data. Algorithm for generation of threshold prototype-based rules are described and a comparison with neurofuzzy systems on a number of datasets provided. Results show that systems for data understanding generating prototypes deserve at least the same attention as that enjoyed by the neurofuzzy systems.

This paper studies how to compare and select one best alternative, from the new alternatives, according to historical or current ones. Previous methods not only need a lot of data but also are complex. So, we put forward an RBF neural network method that not only has the advantages of common neural network methods, but also need much less samples and are straightforward. The number of neurons at the hidden level is easily determined. This model can determine attribute weights automatically so that weights are more objectively and accurately distributed. Further, decision maker’s specific preferences for uncertainty, i.e., risk-averse, risk-loving or risk-neutral, are considered in the determination of weights. Hence, our method can give objective results while taking into decision maker’s subjective intensions. A numerical example is given to illustrate the method.

After the network has been constructed, with the increasing demand, the network must be faced with the capacity expansion problem. In this paper, a mathematic model is formulated to solve the bottleneck capacity expansion problem of network with fuzzy demand. A linear program model with fuzzy coefficient is put forward. We present a decomposition algorithm to solve the model. The results show the decomposition algorithm can improve the solving speed greatly. So, we can minimize the expansion cost and provide evidence for the decision maker to make reasonable and effective decision.