Second language acquisition can not be understood without addressing the interaction between language and cognition. Cognitive theory can extend to describe learning strategies as complex cognitive skills. Theoretical...Second language acquisition can not be understood without addressing the interaction between language and cognition. Cognitive theory can extend to describe learning strategies as complex cognitive skills. Theoretical developments in Anderson’s production systems cover a broader range of behavior than other theories, including comprehension and production of oral and written texts as well as comprehension, problem solving, and verbal learning.Thus Anderson’s cognitive theory can be served as a rationale for learning strategy studies in second language acquisition.展开更多
The support vector machine （SVM） is a novel machine learning method, which has the ability to approximate nonlinear functions with arbitrary accuracy. Setting parameters well is very crucial for SVM learning results...The support vector machine （SVM） is a novel machine learning method, which has the ability to approximate nonlinear functions with arbitrary accuracy. Setting parameters well is very crucial for SVM learning results and generalization ability, and now there is no systematic, general method for parameter selection. In this article, the SVM parameter selection for function approximation is regarded as a compound optimization problem and a mutative scale chaos optimization algorithm is employed to search for optimal paraxneter values. The chaos optimization algorithm is an effective way for global optimal and the mutative scale chaos algorithm could improve the search efficiency and accuracy. Several simulation examples show the sensitivity of the SVM parameters and demonstrate the superiority of this proposed method for nonlinear function approximation.展开更多
A relaxation least squares-based learning algorithm for neual networks is proposed. Not only does it have a fast convergence rate, but it involves less computation quantity. Therefore, it is suitable to deal with the ...A relaxation least squares-based learning algorithm for neual networks is proposed. Not only does it have a fast convergence rate, but it involves less computation quantity. Therefore, it is suitable to deal with the case when a network has a large scale but the number of training data is very limited. It has been used in converting furnace process modelling, and impressive result has been obtained.展开更多
In this paper, two theorems are proved for zero cost function (or precise I/O mapping) training algorithms about three-layered feedforward neural networks. Two training algorithms based on Moore-Penrose pseudoinverse ...In this paper, two theorems are proved for zero cost function (or precise I/O mapping) training algorithms about three-layered feedforward neural networks. Two training algorithms based on Moore-Penrose pseudoinverse (MPPI) matrix together with corresponding structure design guidelines are also proposed.展开更多
文摘Second language acquisition can not be understood without addressing the interaction between language and cognition. Cognitive theory can extend to describe learning strategies as complex cognitive skills. Theoretical developments in Anderson’s production systems cover a broader range of behavior than other theories, including comprehension and production of oral and written texts as well as comprehension, problem solving, and verbal learning.Thus Anderson’s cognitive theory can be served as a rationale for learning strategy studies in second language acquisition.
基金the National Nature Science Foundation of China (60775047, 60402024)
文摘The support vector machine （SVM） is a novel machine learning method, which has the ability to approximate nonlinear functions with arbitrary accuracy. Setting parameters well is very crucial for SVM learning results and generalization ability, and now there is no systematic, general method for parameter selection. In this article, the SVM parameter selection for function approximation is regarded as a compound optimization problem and a mutative scale chaos optimization algorithm is employed to search for optimal paraxneter values. The chaos optimization algorithm is an effective way for global optimal and the mutative scale chaos algorithm could improve the search efficiency and accuracy. Several simulation examples show the sensitivity of the SVM parameters and demonstrate the superiority of this proposed method for nonlinear function approximation.
基金This project was supported by the National Natural Science Foundation of China (No. 60174021)the Key Project of Tianjin Natural Science Foundation (No.010115).
文摘A relaxation least squares-based learning algorithm for neual networks is proposed. Not only does it have a fast convergence rate, but it involves less computation quantity. Therefore, it is suitable to deal with the case when a network has a large scale but the number of training data is very limited. It has been used in converting furnace process modelling, and impressive result has been obtained.
文摘In this paper, two theorems are proved for zero cost function (or precise I/O mapping) training algorithms about three-layered feedforward neural networks. Two training algorithms based on Moore-Penrose pseudoinverse (MPPI) matrix together with corresponding structure design guidelines are also proposed.
