为了增强情感识别过程中皮肤电反应(GSR)信号特征选择的有效性，提出了一种改进的模拟退火免疫粒子群算法。
An improved immune particle swarm optimization was presented in this study in order to increase the effectiveness of feature selection for emotion recognition based on Galvanic Skin Response (GSR).
这类算法主要包括进化算法(EA)、粒子群算法(PSO)、人工免疫系统(ais)和蚁群算法(aco)等等。
Such algorithms include evolutionary algorithm (EA), particle swarm optimization (PSO), artificial immune system (AIS) and ant colony optimization (ACO) and so on.
针对多峰函数优化问题，借鉴粒子群优化特性和免疫网络理论，提出一种免疫粒子群网络算法。
Referred to the character of particle swarm optimization and immune network theory, an immune particle swarm network algorithm for multimodal function optimization is proposed.
在对基本的算法的分析比较基础上，分析了一种基于克隆的免疫遗传算法、基于高低位变异的免疫算法、基于粒子群优化的免疫算法。
On this basis, I analyze Immune Algorithm based on Clone, Immune Algorithm based on High and Low Position and Immune Algorithm based on Particle Swarm Optimization.
实验结果表明该算法优于几种典型的粒子群算法和基本免疫克隆算法。
The experiment results demonstrate that the proposed algorithm is superior to several typical modified PSO algorithms and immune clone algorithm.
实验结果表明该算法优于几种典型的粒子群算法和基本免疫克隆算法。
The experiment results demonstrate that the proposed algorithm is superior to several typical modified PSO algorithms and immune clone algorithm.
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