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mhsj
- 针对遗传算法的特点，提出一种用模糊控制的方法来调整交叉概率和变异概率的改进模糊遗传算法及其算法结构，并 将其应用于神经模糊控制器的综合优化设计。-For the characteristics of genetic algorithm, a method using fuzzy control to adjust the crossover probability and mutation probability of improving the fuzzy genetic algorithm
caovanhai_11506161
- neuro network for matlab in controler
power_acdrive
- The induction motor is fed by a current-controlled PWM inverter which is built using a Universal Bridge block. The motor drives a mechanical load characterized by inertia J, friction coeficient B, and load torque TL . The speed control loop uses a pr
Project
- neuro-fuzzy and Genetic algorithm are combined to find best input.
NEURO-GENETIC
- The proposed approach is based on three stages which (1) use neural networks for constructing a response function model of a dynamic multiresponse system, (2) use exponential desirability functions for evaluating overall performance of a specific
NEURO-GENETIC
- The proposed approach is based on three stages which (1) use neural networks for constructing a response function model of a dynamic multiresponse system, (2) use exponential desirability functions for evaluating overall performance of a specific
NEURO-GENETIC
- The proposed approach is based on three stages which (1) use neural networks for constructing a response function model of a dynamic multiresponse system, (2) use exponential desirability functions for evaluating overall performance of a specific
NEURO-GENETIC
- The proposed approach is based on three stages which (1) use neural networks for constructing a response function model of a dynamic multiresponse system, (2) use exponential desirability functions for evaluating overall performance of a specific
NEURO-GENETIC
- The proposed approach is based on three stages which (1) use neural networks for constructing a response function model of a dynamic multiresponse system, (2) use exponential desirability functions for evaluating overall performance of a specific
NEURO-GENETIC
- The proposed approach is based on three stages which (1) use neural networks for constructing a response function model of a dynamic multiresponse system, (2) use exponential desirability functions for evaluating overall performance of a specific
NEURO-GENETIC
- The proposed approach is based on three stages which (1) use neural networks for constructing a response function model of a dynamic multiresponse system, (2) use exponential desirability functions for evaluating overall performance of a specific
NEURO-GENETIC
- The proposed approach is based on three stages which (1) use neural networks for constructing a response function model of a dynamic multiresponse system, (2) use exponential desirability functions for evaluating overall performance of a specific
NEURO-GENETIC
- The proposed approach is based on three stages which (1) use neural networks for constructing a response function model of a dynamic multiresponse system, (2) use exponential desirability functions for evaluating overall performance of a specific
NEURO-GENETIC
- The proposed approach is based on three stages which (1) use neural networks for constructing a response function model of a dynamic multiresponse system, (2) use exponential desirability functions for evaluating overall performance of a specific
NEURO-GENETIC
- The proposed approach is based on three stages which (1) use neural networks for constructing a response function model of a dynamic multiresponse system, (2) use exponential desirability functions for evaluating overall performance of a specific
NEURO-GENETIC
- The proposed approach is based on three stages which (1) use neural networks for constructing a response function model of a dynamic multiresponse system, (2) use exponential desirability functions for evaluating overall performance of a specific
NEURO-GENETIC
- The proposed approach is based on three stages which (1) use neural networks for constructing a response function model of a dynamic multiresponse system, (2) use exponential desirability functions for evaluating overall performance of a specific
NEURO-GENETIC
- The proposed approach is based on three stages which (1) use neural networks for constructing a response function model of a dynamic multiresponse system, (2) use exponential desirability functions for evaluating overall performance of a specific
MATLAB-NN
- MATLAB神经网络的各类程序，包含遗传算法，神经网络，神经模糊，SVM等程序，十分实用。-All kinds of MATLAB neural network program, including genetic algorithms, neural networks, neuro-fuzzy, SVM and other procedures, very useful.
AForge.NET Framework-2.2.5
- AForge.NET是一个专门为开发者和研究者基于C#框架设计的，这个框架提供了不同的类库和关于类库的资源，还有很多应用程序例子，包括计算机视觉与人工智能，图像处理，神经网络，遗传算法，机器学习，机器人等领域。 这个框架由一系列的类库组成。主要包括有: AForge.Imaging -- 一些日常的图像处理和过滤器 AForge.Vision -- 计算机视觉应用类库 AForge.Neuro -- 神经网络计算库AForge.Genetic -进化算法编程库 AF