言语产生是一项高度复杂的感觉运动任务,在言语产生过程中,运动编程将抽象的语音代码转化为特定的运动指令,一直被认为是一个关键步骤。运动编程在理解言语失用症(apraxia of speech)具有特殊的相关性。言语失用症是一种运动语音障碍,...言语产生是一项高度复杂的感觉运动任务,在言语产生过程中,运动编程将抽象的语音代码转化为特定的运动指令,一直被认为是一个关键步骤。运动编程在理解言语失用症(apraxia of speech)具有特殊的相关性。言语失用症是一种运动语音障碍,它的特点是在言语产生的层级加工过程中,发音和韵律的破坏[1]。近几十年,由于神经成像和计算方法的出现,促进了言语运动编程和执行的神经计算模型的开发。在当前,具有生物学意义的言语生成和获得神经网络模型中。展开更多
A neurocomputing model for Genetic Algorithm (GA) to break the speed bottleneck of GA was proposed. With all genetic operations parallel implemented by NN-based sub-modules, the model integrates both the strongpoint o...A neurocomputing model for Genetic Algorithm (GA) to break the speed bottleneck of GA was proposed. With all genetic operations parallel implemented by NN-based sub-modules, the model integrates both the strongpoint of parallel GA (PGA) and those of hardware GA (HGA). Moreover a new crossover operator named universe crossover was also proposed to suit the NN-based realization. This model was tested with a benchmark function set, and the experimental results validated the potential of the neurocomputing model. The significance of this model means that HGA and PGA can be integrated and the inherent parallelism of GA can be explicitly and farthest realized, as a result, the optimization speed of GA will be accelerated by one or two magnitudes compered to the serial implementation with same speed hardware, and GA will be turned from an algorithm into a machine.展开更多
文摘言语产生是一项高度复杂的感觉运动任务,在言语产生过程中,运动编程将抽象的语音代码转化为特定的运动指令,一直被认为是一个关键步骤。运动编程在理解言语失用症(apraxia of speech)具有特殊的相关性。言语失用症是一种运动语音障碍,它的特点是在言语产生的层级加工过程中,发音和韵律的破坏[1]。近几十年,由于神经成像和计算方法的出现,促进了言语运动编程和执行的神经计算模型的开发。在当前,具有生物学意义的言语生成和获得神经网络模型中。
基金NationalNaturalScienceFoundationofChina (No .60 2 3 40 2 0 )
文摘A neurocomputing model for Genetic Algorithm (GA) to break the speed bottleneck of GA was proposed. With all genetic operations parallel implemented by NN-based sub-modules, the model integrates both the strongpoint of parallel GA (PGA) and those of hardware GA (HGA). Moreover a new crossover operator named universe crossover was also proposed to suit the NN-based realization. This model was tested with a benchmark function set, and the experimental results validated the potential of the neurocomputing model. The significance of this model means that HGA and PGA can be integrated and the inherent parallelism of GA can be explicitly and farthest realized, as a result, the optimization speed of GA will be accelerated by one or two magnitudes compered to the serial implementation with same speed hardware, and GA will be turned from an algorithm into a machine.
