Logic Simulation

14,000,000 Leading Edge Experts on the ideXlab platform

Scan Science and Technology

Contact Leading Edge Experts & Companies

Scan Science and Technology

Contact Leading Edge Experts & Companies

The Experts below are selected from a list of 56856 Experts worldwide ranked by ideXlab platform

Oscar Castillo - One of the best experts on this subject based on the ideXlab platform.

  • interval type 2 fuzzy Logic for dynamic parameter adaptation in a modified gravitational search algorithm
    Information Sciences, 2019
    Co-Authors: Frumen Olivas, Fevrier Valdez, Patricia Melin, Alberto Sombra, Oscar Castillo
    Abstract:

    Abstract In this paper a method for dynamically adjusting parameters in meta-heuristics based on interval type-2 fuzzy Logic is proposed. Nowadays meta-heuristic algorithms have become a powerful choice in solving complex optimization problems. The gravitational search algorithm (GSA) based on the Newton laws of gravity and acceleration can be used to solve optimization problems achieving good results, however like in other optimization algorithms a critical issue is an appropriate adjustment of its parameters depending on the type of problem. In this paper the main contribution is a proposed method aimed at dynamic parameter adjustment in GSA with the help of type-2 fuzzy Logic. Simulation results on benchmark problems show the advantages of the proposed approach.

  • bat algorithm with parameter adaptation using interval type 2 fuzzy Logic for benchmark mathematical functions
    IEEE International Conference on Intelligent Systems, 2016
    Co-Authors: Jonathan Perez, Fevrier Valdez, Oscar Castillo, Olympia Roeva
    Abstract:

    In this paper we propose a new method for dynamic parameter adaptation in the bat algorithm (BA). BA is a metaheuristic algorithm inspired by the behavior of micro bats, which has been applied to different optimization problems obtaining good results. In this paper we propose dynamic parameter adaptation of the BA using Interval Type-2 fuzzy Logic. Simulation results show that the proposed method using Type-2 fuzzy Logic is better in comparison with Type-1 fuzzy Logic.

  • dynamic parameter adaptation in particle swarm optimization using interval type 2 fuzzy Logic
    Soft Computing, 2016
    Co-Authors: Frumen Olivas, Fevrier Valdez, Oscar Castillo, Patricia Melin
    Abstract:

    In this paper, we propose a new method for dynamic parameter adaptation in particle swarm optimization (PSO). PSO is an optimization method inspired in social behavior, which has been applied to different optimization problems obtaining good results. In this paper, we propose an improvement to the convergence and diversity of the swarm in PSO using interval type-2 fuzzy Logic. Simulation results show that the proposed approach improves the performance of PSO. A comparison of the proposed method using type-2 fuzzy Logic with the original PSO approach, and with PSO using type-1 fuzzy Logic for dynamic parameter adaptation is presented.

  • fuzzy classification system design using pso with dynamic parameter adaptation through fuzzy Logic
    Fuzzy Logic Augmentation of Nature-Inspired Optimization Metaheuristics, 2015
    Co-Authors: Frumen Olivas, Fevrier Valdez, Oscar Castillo
    Abstract:

    In this paper a new method for dynamic parameter adaptation in particle swarm optimization (PSO) is proposed. PSO is a metaheuristic inspired in social behaviors, which is very useful in optimization problems. In this paper we propose an improvement to the convergence and diversity of the swarm in PSO using fuzzy Logic. Simulation results show that the proposed approach improves the performance of PSO.

  • an improved harmony search algorithm using fuzzy Logic for the optimization of mathematical functions
    Design of Intelligent Systems Based on Fuzzy Logic Neural Networks and Nature-Inspired Optimization, 2015
    Co-Authors: Cinthia Peraza, Fevrier Valdez, Oscar Castillo
    Abstract:

    In this paper a new method for dynamic parameter adaptation in harmony search (HS) algorithm is proposed. HS is a music inspired metaheuristic optimization algorithm, in particular we refer to jazz improvisation and is applied to solve complex problems. In this paper we propose an improvement to the convergence of the harmony search algorithm using fuzzy Logic. Simulation results show that the propose approach improves the performance of HS, and benchmark mathematical functions are used to illustrate the feasibility of the proposed approach.

Frumen Olivas - One of the best experts on this subject based on the ideXlab platform.

  • interval type 2 fuzzy Logic for dynamic parameter adaptation in a modified gravitational search algorithm
    Information Sciences, 2019
    Co-Authors: Frumen Olivas, Fevrier Valdez, Patricia Melin, Alberto Sombra, Oscar Castillo
    Abstract:

    Abstract In this paper a method for dynamically adjusting parameters in meta-heuristics based on interval type-2 fuzzy Logic is proposed. Nowadays meta-heuristic algorithms have become a powerful choice in solving complex optimization problems. The gravitational search algorithm (GSA) based on the Newton laws of gravity and acceleration can be used to solve optimization problems achieving good results, however like in other optimization algorithms a critical issue is an appropriate adjustment of its parameters depending on the type of problem. In this paper the main contribution is a proposed method aimed at dynamic parameter adjustment in GSA with the help of type-2 fuzzy Logic. Simulation results on benchmark problems show the advantages of the proposed approach.

  • dynamic parameter adaptation in particle swarm optimization using interval type 2 fuzzy Logic
    Soft Computing, 2016
    Co-Authors: Frumen Olivas, Fevrier Valdez, Oscar Castillo, Patricia Melin
    Abstract:

    In this paper, we propose a new method for dynamic parameter adaptation in particle swarm optimization (PSO). PSO is an optimization method inspired in social behavior, which has been applied to different optimization problems obtaining good results. In this paper, we propose an improvement to the convergence and diversity of the swarm in PSO using interval type-2 fuzzy Logic. Simulation results show that the proposed approach improves the performance of PSO. A comparison of the proposed method using type-2 fuzzy Logic with the original PSO approach, and with PSO using type-1 fuzzy Logic for dynamic parameter adaptation is presented.

  • fuzzy classification system design using pso with dynamic parameter adaptation through fuzzy Logic
    Fuzzy Logic Augmentation of Nature-Inspired Optimization Metaheuristics, 2015
    Co-Authors: Frumen Olivas, Fevrier Valdez, Oscar Castillo
    Abstract:

    In this paper a new method for dynamic parameter adaptation in particle swarm optimization (PSO) is proposed. PSO is a metaheuristic inspired in social behaviors, which is very useful in optimization problems. In this paper we propose an improvement to the convergence and diversity of the swarm in PSO using fuzzy Logic. Simulation results show that the proposed approach improves the performance of PSO.

  • particle swarm optimization with dynamic parameter adaptation using interval type 2 fuzzy Logic for benchmark mathematical functions
    Nature and Biologically Inspired Computing, 2013
    Co-Authors: Frumen Olivas, Fevrier Valdez, Oscar Castillo
    Abstract:

    In this paper we propose a new method for dynamic parameter adaptation in particle swarm optimization (PSO). PSO is an optimization method inspired in social behaviors, which has been applied to different optimization problems and obtaining good results. In this paper we propose an improvement to the convergence and diversity of the swarm in PSO using interval type-2 fuzzy Logic. Simulation results show that the proposed approach improves the performance of PSO, and present a comparison with original approach, and PSO with dynamic parameter adaptation using type-1 fuzzy Logic.

  • optimal design of fuzzy classification systems using pso with dynamic parameter adaptation through fuzzy Logic
    Expert Systems With Applications, 2013
    Co-Authors: Patricia Melin, Fevrier Valdez, Oscar Castillo, Frumen Olivas, Jose Soria, Mario Garcia Valdez
    Abstract:

    In this paper a new method for dynamic parameter adaptation in particle swarm optimization (PSO) is proposed. PSO is a metaheuristic inspired in social behaviors, which is very useful in optimization problems. In this paper we propose an improvement to the convergence and diversity of the swarm in PSO using fuzzy Logic. Simulation results show that the proposed approach improves the performance of PSO. First, benchmark mathematical functions are used to illustrate the feasibility of the proposed approach. Then a set of classification problems are used to show the potential applicability of the fuzzy parameter adaptation of PSO.

Fevrier Valdez - One of the best experts on this subject based on the ideXlab platform.

  • interval type 2 fuzzy Logic for dynamic parameter adaptation in a modified gravitational search algorithm
    Information Sciences, 2019
    Co-Authors: Frumen Olivas, Fevrier Valdez, Patricia Melin, Alberto Sombra, Oscar Castillo
    Abstract:

    Abstract In this paper a method for dynamically adjusting parameters in meta-heuristics based on interval type-2 fuzzy Logic is proposed. Nowadays meta-heuristic algorithms have become a powerful choice in solving complex optimization problems. The gravitational search algorithm (GSA) based on the Newton laws of gravity and acceleration can be used to solve optimization problems achieving good results, however like in other optimization algorithms a critical issue is an appropriate adjustment of its parameters depending on the type of problem. In this paper the main contribution is a proposed method aimed at dynamic parameter adjustment in GSA with the help of type-2 fuzzy Logic. Simulation results on benchmark problems show the advantages of the proposed approach.

  • bat algorithm with parameter adaptation using interval type 2 fuzzy Logic for benchmark mathematical functions
    IEEE International Conference on Intelligent Systems, 2016
    Co-Authors: Jonathan Perez, Fevrier Valdez, Oscar Castillo, Olympia Roeva
    Abstract:

    In this paper we propose a new method for dynamic parameter adaptation in the bat algorithm (BA). BA is a metaheuristic algorithm inspired by the behavior of micro bats, which has been applied to different optimization problems obtaining good results. In this paper we propose dynamic parameter adaptation of the BA using Interval Type-2 fuzzy Logic. Simulation results show that the proposed method using Type-2 fuzzy Logic is better in comparison with Type-1 fuzzy Logic.

  • dynamic parameter adaptation in particle swarm optimization using interval type 2 fuzzy Logic
    Soft Computing, 2016
    Co-Authors: Frumen Olivas, Fevrier Valdez, Oscar Castillo, Patricia Melin
    Abstract:

    In this paper, we propose a new method for dynamic parameter adaptation in particle swarm optimization (PSO). PSO is an optimization method inspired in social behavior, which has been applied to different optimization problems obtaining good results. In this paper, we propose an improvement to the convergence and diversity of the swarm in PSO using interval type-2 fuzzy Logic. Simulation results show that the proposed approach improves the performance of PSO. A comparison of the proposed method using type-2 fuzzy Logic with the original PSO approach, and with PSO using type-1 fuzzy Logic for dynamic parameter adaptation is presented.

  • fuzzy classification system design using pso with dynamic parameter adaptation through fuzzy Logic
    Fuzzy Logic Augmentation of Nature-Inspired Optimization Metaheuristics, 2015
    Co-Authors: Frumen Olivas, Fevrier Valdez, Oscar Castillo
    Abstract:

    In this paper a new method for dynamic parameter adaptation in particle swarm optimization (PSO) is proposed. PSO is a metaheuristic inspired in social behaviors, which is very useful in optimization problems. In this paper we propose an improvement to the convergence and diversity of the swarm in PSO using fuzzy Logic. Simulation results show that the proposed approach improves the performance of PSO.

  • an improved harmony search algorithm using fuzzy Logic for the optimization of mathematical functions
    Design of Intelligent Systems Based on Fuzzy Logic Neural Networks and Nature-Inspired Optimization, 2015
    Co-Authors: Cinthia Peraza, Fevrier Valdez, Oscar Castillo
    Abstract:

    In this paper a new method for dynamic parameter adaptation in harmony search (HS) algorithm is proposed. HS is a music inspired metaheuristic optimization algorithm, in particular we refer to jazz improvisation and is applied to solve complex problems. In this paper we propose an improvement to the convergence of the harmony search algorithm using fuzzy Logic. Simulation results show that the propose approach improves the performance of HS, and benchmark mathematical functions are used to illustrate the feasibility of the proposed approach.

Patricia Melin - One of the best experts on this subject based on the ideXlab platform.

  • interval type 2 fuzzy Logic for dynamic parameter adaptation in a modified gravitational search algorithm
    Information Sciences, 2019
    Co-Authors: Frumen Olivas, Fevrier Valdez, Patricia Melin, Alberto Sombra, Oscar Castillo
    Abstract:

    Abstract In this paper a method for dynamically adjusting parameters in meta-heuristics based on interval type-2 fuzzy Logic is proposed. Nowadays meta-heuristic algorithms have become a powerful choice in solving complex optimization problems. The gravitational search algorithm (GSA) based on the Newton laws of gravity and acceleration can be used to solve optimization problems achieving good results, however like in other optimization algorithms a critical issue is an appropriate adjustment of its parameters depending on the type of problem. In this paper the main contribution is a proposed method aimed at dynamic parameter adjustment in GSA with the help of type-2 fuzzy Logic. Simulation results on benchmark problems show the advantages of the proposed approach.

  • dynamic parameter adaptation in particle swarm optimization using interval type 2 fuzzy Logic
    Soft Computing, 2016
    Co-Authors: Frumen Olivas, Fevrier Valdez, Oscar Castillo, Patricia Melin
    Abstract:

    In this paper, we propose a new method for dynamic parameter adaptation in particle swarm optimization (PSO). PSO is an optimization method inspired in social behavior, which has been applied to different optimization problems obtaining good results. In this paper, we propose an improvement to the convergence and diversity of the swarm in PSO using interval type-2 fuzzy Logic. Simulation results show that the proposed approach improves the performance of PSO. A comparison of the proposed method using type-2 fuzzy Logic with the original PSO approach, and with PSO using type-1 fuzzy Logic for dynamic parameter adaptation is presented.

  • optimal design of fuzzy classification systems using pso with dynamic parameter adaptation through fuzzy Logic
    Expert Systems With Applications, 2013
    Co-Authors: Patricia Melin, Fevrier Valdez, Oscar Castillo, Frumen Olivas, Jose Soria, Mario Garcia Valdez
    Abstract:

    In this paper a new method for dynamic parameter adaptation in particle swarm optimization (PSO) is proposed. PSO is a metaheuristic inspired in social behaviors, which is very useful in optimization problems. In this paper we propose an improvement to the convergence and diversity of the swarm in PSO using fuzzy Logic. Simulation results show that the proposed approach improves the performance of PSO. First, benchmark mathematical functions are used to illustrate the feasibility of the proposed approach. Then a set of classification problems are used to show the potential applicability of the fuzzy parameter adaptation of PSO.

Munehiro Matsuura - One of the best experts on this subject based on the ideXlab platform.

  • a pc based Logic simulator using a look up table cascade emulator
    IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences, 2006
    Co-Authors: Hiroki Nakahara, Tsutomu Sasao, Munehiro Matsuura
    Abstract:

    This paper represents a cycle-based Logic Simulation method using an LUT cascade emulator, where an LUT cascade consists of multiple-output LUTs (cells) connected in series. The LUT cascade emulator is an architecture that emulates LUT cascades. It has a control part, a memory for Logic, and registers. It connects the memory to registers through a programmable interconnection circuit, and evaluates the given circuit stored in the memory. The LUT cascade emulator runs on an ordinary PC. This paper also compares the method with a Levelized Compiled Code (LCC) simulator and a simulator using a Quasi-Reduced Multi-valued Decision Diagram (QRMDD). Our simulator is 3.5 to 10.6 times faster than the LCC, and 1.1 to 3.9 times faster than the one using a QRMDD. The Simulation setup time is 2.0 to 9.8 times shorter than the LCC. The necessary amount of memory is 1/1.8 to 1/5.5 of the one using a QRMDD.

  • a fast Logic simulator using a look up table cascade emulator
    Asia and South Pacific Design Automation Conference, 2006
    Co-Authors: Hiroki Nakahara, Tsutomu Sasao, Munehiro Matsuura
    Abstract:

    This paper shows a new type of a cycle-based Logic Simulation method using a look-up table (LUT) cascade emulator. The method first transforms a given circuit into LUT cascades through BDD (binary decision diagram). Then, it stores LUT data to the memory of an LUT cascade emulator. Next, it generates the C code representing the control circuit of the LUT cascade emulator. And, finally, it converts the C code into the execution code. This method is compared with a levelized compiled code (LCC) simulator with respect to the Simulation time and setup time. Although we used standard PC to simulate the circuit, experimental results show that this method is 12-64 times faster than the LCC.

  • bdd representation for incompletely specified multiple output Logic functions and its applications to functional decomposition
    Design Automation Conference, 2005
    Co-Authors: Tsutomu Sasao, Munehiro Matsuura
    Abstract:

    A multiple-output function can be represented by a binary decision diagram for characteristic function (BDD/spl I.bar/for/spl I.bar/CF). This paper presents a new method to represent multiple-output incompletely specified functions using BDD/spl I.bar/for/spl I.bar/CF. An algorithm to reduce the widths of BDD/spl I.bar/for/spl I.bar/CFs is presented. This method is useful for decomposition of incompletely specified multiple-output functions. Experimental results for radix converters, adders and a multiplier show that this method is useful for the synthesis of LUT cascades. This data structure is also useful to three-valued Logic Simulation.

Related terms

Logic Simulation - 15 days free trial to Access Experts & Abstracts