Catálogo de publicaciones - libros

In this paper, a kinematic controller based on input-output linearization plus neural network (NN) controller is presented for tracking control of a mobile robot with kinematic uncertainty. The NN controller, whose parameters are tuned on-line, can deal with the uncertainty imposed on the kinematics model of mobile robots. The stability of the proposed approach is guaranteed by the Lyapunov theory. Simulation results show the efficiency of the proposed approach.

In this paper, using the pre-determined specific tasks, a solid and complete solution for the optimal control of the mobile manipulator is proposed based on a divide and conquer scheme. In the scheme, a mobile manipulator is virtually divided into a mobile robot and a task robot. All the tasks are also divided into task segments that can be performed by only the task robot. An optimal configuration of the task robot is defined by the task oriented manipulability measure for given task segment. And using a cost function for optimality defined as a combination of the square errors of the desired and actual configurations of the mobile robot and of the task robot, the job which the mobile manipulator performs is optimized. We figured out the solution for the optimal configuration of a mobile manipulator with a series of tasks.

This paper presents a GA-based synthesis algorithm of a cellular automaton ( CA ) that can generate a desired spatio-temporal pattern. Time evolution of CA is determined by a rule table the number of which is enormous even for relatively small size CAs: the brute-force search is almost impossible. In our GA-based synthesis algorithm, a gene corresponds to a rule and a masking technique is used to preserve gene(s) with good fitness. Performing basic numerical experiments we have confirmed that the masking works effectively and the algorithm can generate a desired rule table. We have also considered an application to reduction of noise inserted randomly to a spatio-temporal pattern.

In recent years, network analysis has revealed that some real networks have the properties of small-world and/or scale-free networks. In this paper, a simple Genetic Algorithm (GA) is regarded as a network where each node and each edge respectively represent a population and the possibility of the transition between two nodes. The characteristic path length, which is one of the most popular criterion in small-world networks, is derived analytically. The results show how the crossover operation works in GAs to shorten the path length between two populations, compared to the length of the network with the mutation operation.

Many researches on genetic algorithm based multi-user detection indicate initial population has crucial effects on the performance of detectors. Commonly used method to obtain initial population is to perturb the input chromosome randomly, which fails to fully exploit the effective information delivered by input chromosome. This paper proposes a kind of Hamming Sphere Solution Space based Genetic Multi-User Detector (HSSSGMUD), which constructs the initial population in a simple but effective manner. Firstly, select the input chromosome, and regard it as the center of a sphere in dimensions space, where is data packet length and is user numbers in Code Division Multiple Access (CDMA) system. Then, the concept of Hamming sphere space is used to obtain other chromosomes of initial population. Simulation results show the proposed HSSSGMUD not only achieves lower Bit Error Ratio (BER) and better near-far resistant ability, but also converges quickly.

We introduce a notion of implicit elitism derived from the mutation operator in genetic algorithms. Probability of mutation less than 1/ ( being the chromosome size) along with probability of crossover less than one induces implicit elitism in genetic search. It implicitly transfers a few chromosomes with above-average fitness unperturbed to the population at next generation, thus maintaining the progress of genetic search. Experiments conducted on one-max and 0/1 knapsack problems testify its efficacy. Implicit elitism in combination with traditional explicit elitism enhances the search capability of genetic algorithms.

TSP(Traveling Salesman Problem) used widely for solving the optimization is the problem to find out the shortest distance out of possible courses where one starts a certain city, visits every city among cities and turns back to a staring city. At this time, the condition is to visit cities exactly only once. TSP is defined easily, but as the number of visiting cities increases, the calculation rate increases geometrically. This is why TSP is classified into NP-Hard Problem. Genetic Algorithm is used representatively to solve the TSP. Various operators have been developed and studied until now for solving the TSP more effectively. This paper applied the new Population Initialization Method (using the Random Initialization method and Induced Initialization method simultaneously), solved TSP more effectively, and proved the improvement of capability by comparing this new method with existing methods.

Fuzzy rules are suitable for describing uncertain phenomena and natural for human understanding and they are, in general, efficient for classification. In addition, fuzzy rules allow us to effectively classify data having non-axis-parallel decision boundaries, which is difficult for the conventional attribute-based methods. In this paper, we propose an optimized fuzzy rule generation method for classification both in accuracy and comprehensibility (or rule complexity). We investigate the use of genetic algorithm to determine an optimal set of membership functions for quantitative data. In our method, for a given set of membership functions a fuzzy decision tree is constructed and its accuracy and rule complexity are evaluated, which are combined into the fitness function to be optimized. We have experimented our algorithm with several benchmark data sets. The experiment results show that our method is more efficient in performance and complexity of rules compared with the existing methods.

This paper presents a genetic-inspired multicast routing algorithm with Quality of Service (i.e., bandwidth and end-to-end delay) constraints. The aim is to efficiently discover a minimum-cost multicast tree (a set of paths) that satisfactorily helps various services from a designated source to multiple destinations. To achieve this goal, state of the art genetic-based optimization techniques are employed. Each chromosome is represented as a tree structure of Genetic Programming. A fitness function that returns a tree cost has been suggested. New variation operators (i.e., crossover and mutation) are designed in this regard. Crossover exchanges partial chromosomes (i.e., sub-trees) in a positionally independent manner. Mutation introduces (in part) a new sub-tree with low probability. Moreover, all the infeasible chromosomes are treated with a simple repair function. The synergy achieved by combing new ingredients (i.e., representation, crossover, and mutation) offers an effective search capability that results in improved quality of solution and enhanced rate of convergence. Experimental results show that the proposed GA achieves minimal spanning tree, fast convergence speed, and high reliability. Further, its performance is better than that of a comparative reference.

In this paper, we propose to integrate real coded genetic algorithm (GA) and cultural algorithms (CA) to develop a more efficient algorithm: cultural genetic algorithm (CGA). In this approach, GA’s selection and crossover operations are used in CA’s population space. GA’s mutation is replaced by CA based mutation operation which can attract individuals to move to the semifeasible and feasible region of the optimization problem to avoid the ‘eyeless’ searching in GA. Thus it is possible to enhance search ability and to reduce computational cost. This approach is applied to solve constrained optimization problems. An example is presented to demonstrate the effectiveness of the proposed approach.