Catálogo de publicaciones - libros

This paper presents a tactics using fuzzy logic mediator that selects proper robot action depending on the positions and the roles of adjacent two robots. Conventional Q-learning algorithm, where the number of states increases exponentially with the number of robots, is not suitable for a robot soccer system, because it needs so much calculation that processing cannot be accomplished in real time. A modular Q-learning algorithm reduces a number of states by partitioning the concerned area, where mediator algorithm for cooperation of robots is used additionally. The proposed scheme not only reduces a number of a calculation but also combines a robot action selection with robot cooperation by means of fuzzy logic system.

The regression is one of the fundamental problems in data mining, which is central to many applications of information technology. Various approaches have been presented for regression problem nowadays. However, many problems still exist, such as efficiency and model selection problem. This paper proposes a new approach to regression problem, visual regression problem (VRA) in order to resolve these problems. The core idea is to transfer the regression problem to classification problem based on Ancona theorem, which gives the mathematical equivalence between two problems; and then use visual classification approach, which is an efficient classification approach developed based on mimicking human sensation and perception principle, to solve the transformed classification problem and get an implicit regression function; and finally utilize some mathematical skills to obtain the explicit solution of the regression problem. We also provide a series of simulations to demonstrate that the proposed approach is not only effective but also efficient.

In this paper, we firstly introduce particle swarm optimization to the problem of learning Bayesian networks and propose a novel structure learning algorithm using PSO. To search in DAG spaces efficiently, a discrete PSO algorithm especially for structure learning is proposed based on the characteristics of Bayesian networks. The results of experiments show that our PSO based algorithm is fast for convergence and could obtain better structures compared with GA based algorithms.

A new approach for protection of transmission line including TCSC is presented in this paper. The proposed method includes application of Fuzzy Neural Network for distance relaying of a transmission line operating with a thyristor controlled series capacitor (TCSC) protected by MOVs. Here the fuzzy neural network (FNN) is used for calculating fault location on the TCSC line. The FNN structure is seen as a neural network for training and the fuzzy viewpoint is utilized to gain insight into the system and to simplify the model. The number of rules is determined by the data itself and therefore, a smaller number of rules are produced. The network parameters are updated by Extended Kalman Filter (EKF) algorithm. with a pruning strategy to eliminate the redundant rules and fuzzification neurons resulting in a compact network structure . The input to the FNN are fundamental currents and voltages at the relay end, sequence components of current, system frequency and the firing angle with different operating conditions and the corresponding output is the location of the fault from the relaying point The location tasks of the relay are accomplished using different FNNs for different types of fault (L-G,LL-G,LL, LLL).

Computational intelligence has figured prominently in many solutions to the network intrusion detection problem since the 1990s. This prominence and popularity has continued in the contributions of the recent past. These contributions present the success and potential of computational intelligence in network intrusion detection systems for tasks such as feature selection, signature generation, anomaly detection, classification, and clustering. This paper reviews these contributions categorized in the sub-areas of soft computing, machine learning, artificial immune systems, and agent-based systems.

In this paper, we propose a new constraint handling approach that transforms constrained optimization problem of any number of constraints into a two objective preference optimization problem. We design a new crossover operator based on uniform design methods ([8]), a new mutation operator using local search and preference, and a new selection operator based on the preference of the two objectives. The simulation results indicate the proposed algorithm is effective.

Differential Evolution (DE) is generally considered as a reliable, accurate, robust and fast optimization technique. DE has been successfully applied to solve a wide range of numerical optimization problems. However, the user is required to set the values of the control parameters of DE for each problem. Such parameter tuning is a time consuming task. In this paper, a self-adaptive DE (SDE) is proposed where parameter tuning is not required. The performance of SDE is investigated and compared with other versions of DE. The experiments conducted show that SDE outperformed the other DE versions in all the benchmark functions.

Evolvable hardware (EHW) has been employed in the circuit design automation domain, as an alternative to traditional human being designer. However, limited by the scalability of EHW, at present the scales of all the evolved circuits are smaller than the circuits designed by traditional method. In this paper, a character classification system for recognizing 16 characters was evolved by a novel evolution scheme: reconfigurable architecture-based intrinsic incremental evolution. The entire EHW system is implemented on one Xilinx Virtex xcv2000E FPGA that is fitted in the Celoxica RC1000 board. Hardware evolutionary result proved that the new method could bring us a scalable approach to EHW by efficiently limiting the chromosome string length and reducing the time complexity of evolutionary algorithm (EA).

This paper revisits the problem of Traffic Engineering (TE) to assess the relevance of using Gene Expression Programming () as a new fine-tuning algorithm in destination-based TE. We present a new TE scheme where link weights are computed using and used as fine-tuning parameters in destination-based path selection. We apply the newly proposed TE scheme to compute the routing paths for the traffic offered to a 23- and 30-node test networks under different traffic conditions and differentiated services situations. We evaluate the performance achieved by the algorithm compared to a memetic and the Open Shortest Path First () algorithms in a simulated routing environment using the NS packet level simulator. Preliminary results reveal the relative efficiency of compared to the memetic algorithm and routing.

A new nonslicing floorplan representation, moving block sequence (MBS), is first proposed. The MBS is suitable for evolutionary algorithms since no extra constraints are exerted on the solution space. Furthermore, an organizational evolutionary algorithm is designed with the intrinsic properties of MBS in mind, called MBS-OEA. In experiments, 21 benchmarks from MCNC and GSRC are used to test the performance of MBS-OEA. The number of blocks in these benchmarks varies from 9 to 300. Comparisons are also made between MBS-OEA and some well-designed existing algorithms. The experimental results show that MBS-OEA can find high quality solutions even for the problems with 300 blocks. Therefore, MBS-OEA is competent for solving large scale problems.