Network information criterion-determining the number of hiddenunits for an artificial neural network model

The problem of model selection, or determination of the number of hidden units, can be approached statistically, by generalizing Akaike's information criterion (AIC) to be applicable to unfaithful (i.e., unrealizable) models with general loss criteria including regularization terms. The relation between the training error and the generalization error is studied in terms of the number of the training examples and the complexity of a network which reduces to the number of parameters in the ordinary statistical theory of AIC. This relation leads to a new network information criterion which is useful for selecting the optimal network model based on a given training set.     

H∞ control versus disturbance-observer-basedcontrol

In the motion control field, a disturbance observer-based disturbance canceling control is often used as a robust control methodology. However, this method is nothing more than an alternative design of an integral controller, and the robust stability issue cannot be directly accounted for. In this paper, an extended H_∞ control scheme is proposed as a new robust motion control method which achieves the disturbance cancellation ability and guarantees robust stability automatically     

High performance liquid chromatographic separation of stereoisomeric bile acids as their UV-sensitive esters

High performance liquid chromatographic separation of a series of mono-, di- and trihydroxylated 5β-cholanic acids which differ only in position and configuration of hydroxyl groups at positions C-3, C-7 and/or C-12, is reported. The C-24 free acids were derivatized to four different classes of UV-sensitive esters, i.e.,p-bromophenacyl (BP),m-methoxyphenacyl (MP), 4-nitrophthalimidemethyl (NPM) and 9-anthrylmethyl (AM) esters, and chromatographed on two, variants of C₁₈ reversed-phase columns (Nova-Pak C₁₈ and Zorbax ODS) with methanol-water systems as mobile phase. Separation efficiency and elution order of some isomeric pairs were influenced by both the structure of the C-24 ester groups and the nature of the columns used. Excellent chromatographic properties were found for those derivatives, particularly for the NPM esters.     

Visualizing the structure of Web communities based on data acquired from a search engine

Discovery of Web communities, groups of Web pages sharing common interests, is important for assisting users' information retrieval from the Web. This paper describes a method for visualizing Web communities and their internal structures. visualization of Web communities in the form of graphs enables users to access related pages easily, and it often reflects the characteristics of the Web communities. Since related Web pages are often co-referred from the same Web page, the number of co-occurrences of references in a search engine is used for measuring the relation among pages. Two URLs are given to a search engine as keywords, and the value of the number of pages searched from both URLs divided by the number of pages searched from either URL, which is called the Jaccard coefficient, is calculated as the criteria for evaluating the relation between the two URLs. The value is used for determining the length of an edge in a graph so that vertices of related pages will be located close to each other. Our visualization system based on the method succeeds in clarifying various genres of Web communities, although the system does not interpret the contents of the pages. The method of calculating the Jaccard coefficient is easily processed by computer systems, and it is suitable for visualization using the data acquired from a search engine.     

Balance between genetic search and local search in memetic algorithms for multiobjective permutation flowshop scheduling

This paper shows how the performance of evolutionary multiobjective optimization (EMO) algorithms can be improved by hybridization with local search. The main positive effect of the hybridization is the improvement in the convergence speed to the Pareto front. On the other hand, the main negative effect is the increase in the computation time per generation. Thus, the number of generations is decreased when the available computation time is limited. As a result, the global search ability of EMO algorithms is not fully utilized. These positive and negative effects are examined by computational experiments on multiobjective permutation flowshop scheduling problems. Results of our computational experiments clearly show the importance of striking a balance between genetic search and local search. In this paper, we first modify our former multiobjective genetic local search (MOGLS) algorithm by choosing only good individuals as initial solutions for local search and assigning an appropriate local search direction to each initial solution. Next, we demonstrate the importance of striking a balance between genetic search and local search through computational experiments. Then we compare the modified MOGLS with recently developed EMO algorithms: the strength Pareto evolutionary algorithm and revised nondominated sorting genetic algorithm. Finally, we demonstrate that a local search can be easily combined with those EMO algorithms for designing multiobjective memetic algorithms.     

Performance evaluation of fuzzy rule-based classification systems obtained by multi-objective genetic algorithms

In this paper, we examine the classification performance of fuzzy if-then rules selected by a GA-based multi-objective rule selection method. This rule selection method can be applied to high-dimensional pattern classification problems with many continuous attributes by restricting the number of antecedent conditions of each candidate fuzzy if-then rule. As candidate rules, we only use fuzzy if-then rules with a small number of antecedent conditions. Thus it is easy for human users to understand each rule selected by our method. Our rule selection method has two objectives: to minimize the number of selected fuzzy if-then rules and to maximize the number of correctly classified patterns. In our multi-objective fuzzy rule selection problem, there exist several solutions (i.e., several rule sets) called “non-dominated solutions” because two conflicting objectives are considered. In this paper, we examine the performance of our GA-based rule selection method by computer simulations on a real-world pattern classification problem with many continuous attributes. First we examine the classification performance of our method for training patterns by computer simulations. Next we examine the generalization ability for test patterns. We show that a fuzzy rule-based classification system with an appropriate number of rules has high generalization ability.