The Experts below are selected from a list of 108 Experts worldwide ranked by ideXlab platform

Wang Kangping - One of the best experts on this subject based on the ideXlab platform.

  • Solving Optimization Problem Using Multi-agent Model Based on Belief Interaction
    Lecture Notes in Computer Science, 2006
    Co-Authors: Guo Dongwei, Liu Yanbin, Wang Kangping
    Abstract:

    Multi-Agent model based on belief interaction is used to solve the function Optimization Problems in this paper. Belief is led into the Agent, being the parameter of learning machine to decide the searching direction and intensity of the Agent in the environment. It is also the interaction information between Agents. Agent has the ability to evaluate its path in the past. In this way, Agent can find Optimization object rapidly and avoid partial extremum at the same time. Finally, several benchmark Problems are considered to evaluate the performance of this model. The experimental results prove the efficiency of this model when Solving Optimization Problems.

  • SEAL - Solving Optimization Problem using multi-agent model based on belief interaction
    Lecture Notes in Computer Science, 2006
    Co-Authors: Guo Dongwei, Liu Yanbin, Wang Kangping
    Abstract:

    Multi-Agent model based on belief interaction is used to solve the function Optimization Problems in this paper. “Belief” is led into the Agent, being the parameter of learning machine to decide the searching direction and intensity of the Agent in the environment. It is also the interaction information between Agents. Agent has the ability to evaluate its path in the past. In this way, Agent can find Optimization object rapidly and avoid partial extremum at the same time. Finally, several benchmark Problems are considered to evaluate the performance of this model. The experimental results prove the efficiency of this model when Solving Optimization Problems.

Guo Dongwei - One of the best experts on this subject based on the ideXlab platform.

  • Solving Optimization Problem Using Multi-agent Model Based on Belief Interaction
    Lecture Notes in Computer Science, 2006
    Co-Authors: Guo Dongwei, Liu Yanbin, Wang Kangping
    Abstract:

    Multi-Agent model based on belief interaction is used to solve the function Optimization Problems in this paper. Belief is led into the Agent, being the parameter of learning machine to decide the searching direction and intensity of the Agent in the environment. It is also the interaction information between Agents. Agent has the ability to evaluate its path in the past. In this way, Agent can find Optimization object rapidly and avoid partial extremum at the same time. Finally, several benchmark Problems are considered to evaluate the performance of this model. The experimental results prove the efficiency of this model when Solving Optimization Problems.

  • SEAL - Solving Optimization Problem using multi-agent model based on belief interaction
    Lecture Notes in Computer Science, 2006
    Co-Authors: Guo Dongwei, Liu Yanbin, Wang Kangping
    Abstract:

    Multi-Agent model based on belief interaction is used to solve the function Optimization Problems in this paper. “Belief” is led into the Agent, being the parameter of learning machine to decide the searching direction and intensity of the Agent in the environment. It is also the interaction information between Agents. Agent has the ability to evaluate its path in the past. In this way, Agent can find Optimization object rapidly and avoid partial extremum at the same time. Finally, several benchmark Problems are considered to evaluate the performance of this model. The experimental results prove the efficiency of this model when Solving Optimization Problems.

Liu Yanbin - One of the best experts on this subject based on the ideXlab platform.

  • Solving Optimization Problem Using Multi-agent Model Based on Belief Interaction
    Lecture Notes in Computer Science, 2006
    Co-Authors: Guo Dongwei, Liu Yanbin, Wang Kangping
    Abstract:

    Multi-Agent model based on belief interaction is used to solve the function Optimization Problems in this paper. Belief is led into the Agent, being the parameter of learning machine to decide the searching direction and intensity of the Agent in the environment. It is also the interaction information between Agents. Agent has the ability to evaluate its path in the past. In this way, Agent can find Optimization object rapidly and avoid partial extremum at the same time. Finally, several benchmark Problems are considered to evaluate the performance of this model. The experimental results prove the efficiency of this model when Solving Optimization Problems.

  • SEAL - Solving Optimization Problem using multi-agent model based on belief interaction
    Lecture Notes in Computer Science, 2006
    Co-Authors: Guo Dongwei, Liu Yanbin, Wang Kangping
    Abstract:

    Multi-Agent model based on belief interaction is used to solve the function Optimization Problems in this paper. “Belief” is led into the Agent, being the parameter of learning machine to decide the searching direction and intensity of the Agent in the environment. It is also the interaction information between Agents. Agent has the ability to evaluate its path in the past. In this way, Agent can find Optimization object rapidly and avoid partial extremum at the same time. Finally, several benchmark Problems are considered to evaluate the performance of this model. The experimental results prove the efficiency of this model when Solving Optimization Problems.

Mustafa Turker - One of the best experts on this subject based on the ideXlab platform.

  • dynamic neural network based model predictive control of an industrial baker s yeast drying process
    IEEE Transactions on Neural Networks, 2008
    Co-Authors: Ugur Yuzgec, Yasar Becerikli, Mustafa Turker
    Abstract:

    This paper presents dynamic neural-network-based model-predictive control (MPC) structure for a baker's yeast drying process. Mathematical model consists of two partial nonlinear differential equations that are obtained from heat and mass balances inside dried granules. The drying curves that are obtained from granule-based model were used as training data for neural network (NN) models. The target is to predict the moisture content and product activity, which are very important parameters in drying process, for different horizon values. Genetic-based search algorithm determines the optimal drying profile by Solving Optimization Problem in MPC. As a result of the performance evaluation of the proposed control structure, which is compared with the model based on nonlinear partial differential equation (PDE) and with feedforward neural network (FFN) models, it is particularly satisfactory for the drying process of a baker's yeast.

Liu Guang-bin - One of the best experts on this subject based on the ideXlab platform.

  • Overview of Intelligent Algorithms in Nonlinear Model Predictive Control
    Computer Simulation, 2008
    Co-Authors: Liu Guang-bin
    Abstract:

    Nonlinear model predictive control based on intelligent models and Optimization algorithms were introduced in respects to modeling predictive model and Solving Optimization Problem of nonlinear system. Some representative intelligent algorithms of nonlinear model predictive control proposed in recent years were analyzed both in respects of advantages and disadvantages. The existing Problems and research directions of nonlinear model predictive control based on intelligent methods were indicated at last.