The Experts below are selected from a list of 189 Experts worldwide ranked by ideXlab platform

Chakib Bouallou - One of the best experts on this subject based on the ideXlab platform.

  • adaptive Random Search Method genetic algorithms for reaction kinetics modeling co2 absorption systems
    Chemical engineering transactions, 2011
    Co-Authors: C Toromolina, R Riveratinoco, Chakib Bouallou
    Abstract:

    This paper presents a novel hybrid algorithm based on the Adaptive Random Search Method (ARSM) and enhanced with Genetic Algorithms (GA) theory. This algorithm is first validated by modelling Carbonyl sulfide (COS) absorption by N-methyldiethanolamine (MDEA) aqueous solutions. Results show an excellent fit between modelled values and experimental data. Then, the Method is tested to model CO2 absorption by NH3 aqueous solutions. In general, results show that the proposed Method is faster and slightly more accurate than classic Methods such as Downhill simplex, and simple GA.

  • Hybrid adaptive Random Search and genetic Method for reaction kinetics modelling: CO2 absorption systems
    Journal of Cleaner Production, 2011
    Co-Authors: Carol Toro Molina, Rodrigo Rivera-tinoco, Chakib Bouallou
    Abstract:

    This paper presents a hybrid algorithm to be used for kinetics modelling and based on the Adaptive Random Search Method (ARSM) enhanced by the Genetic Algorithms (GA) theory. This algorithm is validated comparing modelled pressure values to experimental pressure data from Carbonyl sulfide (COS) absorption by aqueous solvents. An excellent fit is shown when COS absorption by N-methyldiethanolamine (MDEA) aqueous solutions is modelled. Preliminary results for the modelling of CO2 absorption by NH3 aqueous solutions show an accurate fit and open the door to further studies on more complex reaction mechanisms suggested for the ammonia-carbon dioxide reaction. Besides, the proposed Method leads to lower computational time and slightly more accurate calculated pressure values than classic Methods such as Downhill simplex.

Carol Toro Molina - One of the best experts on this subject based on the ideXlab platform.

  • Hybrid adaptive Random Search and genetic Method for reaction kinetics modelling: CO2 absorption systems
    Journal of Cleaner Production, 2011
    Co-Authors: Carol Toro Molina, Rodrigo Rivera-tinoco, Chakib Bouallou
    Abstract:

    This paper presents a hybrid algorithm to be used for kinetics modelling and based on the Adaptive Random Search Method (ARSM) enhanced by the Genetic Algorithms (GA) theory. This algorithm is validated comparing modelled pressure values to experimental pressure data from Carbonyl sulfide (COS) absorption by aqueous solvents. An excellent fit is shown when COS absorption by N-methyldiethanolamine (MDEA) aqueous solutions is modelled. Preliminary results for the modelling of CO2 absorption by NH3 aqueous solutions show an accurate fit and open the door to further studies on more complex reaction mechanisms suggested for the ammonia-carbon dioxide reaction. Besides, the proposed Method leads to lower computational time and slightly more accurate calculated pressure values than classic Methods such as Downhill simplex.

Kotaro Hirasawa - One of the best experts on this subject based on the ideXlab platform.

  • a new Random Search Method for neural network learning rasid
    International Joint Conference on Neural Network, 1998
    Co-Authors: Kotaro Hirasawa, Masanao Ohbayashi, J Mutata, Y Eki
    Abstract:

    This paper presents a novel Random Searching scheme called RasID for neural networks training. The idea is to introduce a sophisticated probability density function (PDF) for generating Search vector. The PDF provides two parameters for realizing intensified Search in the area where it is likely to find good solutions locally or diversified Search in order to escape from a local minimum based on the success-failure of the past Search. Gradient information is used to improve the Search performance. The proposed scheme is applied to layered neural networks training and is benchmarked against other deterministic and nondeterministic Methods.

  • a new Random Search Method for neural networks learning Random Search with variable Search length rasval
    International Joint Conference on Neural Network, 1998
    Co-Authors: Kotaro Hirasawa, Masanao Ohbayashi, K Togo, Junichi Murata, Ning Shao
    Abstract:

    In this paper, a new Random Search Method RasVal for neural networks (NN) learning is proposed. RasVal (Random Search with variable Search length) is a kind of Random Search and it can find a global minimum instead of a local minimum using the capability of intensified and diversified Searches. The main different point of RasVal from commonly used Random Search Methods (RSM) is that the shape of the probability density function for Random Searching can be adjusted based on the information of success or failure of the Search. First, RasVal is described and after that, performance between RasVal, backpropagation Method (BP) and backpropagation Method with momentum (Mom.BP) are compared. The performance is evaluated by the simulations which include both static and dynamic neural networks (NN) learning problems. In the simulations, NN is trained to realize nonlinear functions and to control a nonlinear crane system by using RasVal, BP and Mom.BP. Simulation results show that RasVal is superior or nearly equal to BP and Mom.BP because of the ability of intensification and diversification of the Search.

  • nonlinear control system with radial basis function controller using Random Search Method of variable Search length
    Proceedings of International Conference on Neural Networks (ICNN'97), 1997
    Co-Authors: Ning Shao, Kotaro Hirasawa, Masanao Ohbayashi, K Togo, Mitsuo Ikeuchi
    Abstract:

    An optimization Method which is a kind of Random Searching is presented. The proposed Method is called RasVal (Random Search Method with variable Search length) and it can Search for a global minimum based on the probability density functions of Searching, which can be modified using informations on success or failure of the past Searching in order to execute intensified and diversified Searching. By applying the proposed Method to a nonlinear crane control system which can be controlled by the universal learning network with radial basis function (RBF), it has been proved that Ras Val is superior in performance to the commonly used backpropagation learning algorithm, and it has also been shown that the Ras Val has better performance of the generalization capability than the gradient Method.

Ning Shao - One of the best experts on this subject based on the ideXlab platform.

  • a new Random Search Method for neural networks learning Random Search with variable Search length rasval
    International Joint Conference on Neural Network, 1998
    Co-Authors: Kotaro Hirasawa, Masanao Ohbayashi, K Togo, Junichi Murata, Ning Shao
    Abstract:

    In this paper, a new Random Search Method RasVal for neural networks (NN) learning is proposed. RasVal (Random Search with variable Search length) is a kind of Random Search and it can find a global minimum instead of a local minimum using the capability of intensified and diversified Searches. The main different point of RasVal from commonly used Random Search Methods (RSM) is that the shape of the probability density function for Random Searching can be adjusted based on the information of success or failure of the Search. First, RasVal is described and after that, performance between RasVal, backpropagation Method (BP) and backpropagation Method with momentum (Mom.BP) are compared. The performance is evaluated by the simulations which include both static and dynamic neural networks (NN) learning problems. In the simulations, NN is trained to realize nonlinear functions and to control a nonlinear crane system by using RasVal, BP and Mom.BP. Simulation results show that RasVal is superior or nearly equal to BP and Mom.BP because of the ability of intensification and diversification of the Search.

  • nonlinear control system with radial basis function controller using Random Search Method of variable Search length
    Proceedings of International Conference on Neural Networks (ICNN'97), 1997
    Co-Authors: Ning Shao, Kotaro Hirasawa, Masanao Ohbayashi, K Togo, Mitsuo Ikeuchi
    Abstract:

    An optimization Method which is a kind of Random Searching is presented. The proposed Method is called RasVal (Random Search Method with variable Search length) and it can Search for a global minimum based on the probability density functions of Searching, which can be modified using informations on success or failure of the past Searching in order to execute intensified and diversified Searching. By applying the proposed Method to a nonlinear crane control system which can be controlled by the universal learning network with radial basis function (RBF), it has been proved that Ras Val is superior in performance to the commonly used backpropagation learning algorithm, and it has also been shown that the Ras Val has better performance of the generalization capability than the gradient Method.

Masanao Ohbayashi - One of the best experts on this subject based on the ideXlab platform.

  • a new Random Search Method for neural network learning rasid
    International Joint Conference on Neural Network, 1998
    Co-Authors: Kotaro Hirasawa, Masanao Ohbayashi, J Mutata, Y Eki
    Abstract:

    This paper presents a novel Random Searching scheme called RasID for neural networks training. The idea is to introduce a sophisticated probability density function (PDF) for generating Search vector. The PDF provides two parameters for realizing intensified Search in the area where it is likely to find good solutions locally or diversified Search in order to escape from a local minimum based on the success-failure of the past Search. Gradient information is used to improve the Search performance. The proposed scheme is applied to layered neural networks training and is benchmarked against other deterministic and nondeterministic Methods.

  • a new Random Search Method for neural networks learning Random Search with variable Search length rasval
    International Joint Conference on Neural Network, 1998
    Co-Authors: Kotaro Hirasawa, Masanao Ohbayashi, K Togo, Junichi Murata, Ning Shao
    Abstract:

    In this paper, a new Random Search Method RasVal for neural networks (NN) learning is proposed. RasVal (Random Search with variable Search length) is a kind of Random Search and it can find a global minimum instead of a local minimum using the capability of intensified and diversified Searches. The main different point of RasVal from commonly used Random Search Methods (RSM) is that the shape of the probability density function for Random Searching can be adjusted based on the information of success or failure of the Search. First, RasVal is described and after that, performance between RasVal, backpropagation Method (BP) and backpropagation Method with momentum (Mom.BP) are compared. The performance is evaluated by the simulations which include both static and dynamic neural networks (NN) learning problems. In the simulations, NN is trained to realize nonlinear functions and to control a nonlinear crane system by using RasVal, BP and Mom.BP. Simulation results show that RasVal is superior or nearly equal to BP and Mom.BP because of the ability of intensification and diversification of the Search.

  • nonlinear control system with radial basis function controller using Random Search Method of variable Search length
    Proceedings of International Conference on Neural Networks (ICNN'97), 1997
    Co-Authors: Ning Shao, Kotaro Hirasawa, Masanao Ohbayashi, K Togo, Mitsuo Ikeuchi
    Abstract:

    An optimization Method which is a kind of Random Searching is presented. The proposed Method is called RasVal (Random Search Method with variable Search length) and it can Search for a global minimum based on the probability density functions of Searching, which can be modified using informations on success or failure of the past Searching in order to execute intensified and diversified Searching. By applying the proposed Method to a nonlinear crane control system which can be controlled by the universal learning network with radial basis function (RBF), it has been proved that Ras Val is superior in performance to the commonly used backpropagation learning algorithm, and it has also been shown that the Ras Val has better performance of the generalization capability than the gradient Method.