Description: 用神经网络和遗传算法解决tsp问题，先使用神经网络训练出较优个体，然后再进化，可以反其道而行，-using neural networks and genetic algorithms to solve the problem tsp first use of neural network training of better individual, then evolution can be just the opposite. Platform: |
Size: 161574 |
Author:问涛 |
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Description: 用遗传算法优化神经网络权值的工具箱，很好用。解决神经网络全局收敛问题，训练速度快-genetic algorithm optimization neural network toolbox value of the right to good use. Neural network address global convergence, speed training Platform: |
Size: 47292 |
Author:无机 |
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Description: 用神经网络和遗传算法解决tsp问题，先使用神经网络训练出较优个体，然后再进化，可以反其道而行，-using neural networks and genetic algorithms to solve the problem tsp first use of neural network training of better individual, then evolution can be just the opposite. Platform: |
Size: 160768 |
Author:问涛 |
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Description: 本文深入研究了 BP 神经网络与遗传算法理论，BP 神经网络在应用过程中面临
着网络训练时间长、容易陷入局部极小值、隐层节点数不易确定等缺点，为了有效
地克服 BP 网的困难，将遗传算法与 BP 网络有机地融合，使它们之间的相互补充增
强彼此的能力，从而获得更有力的解决实际问题的能力。
-this in-depth study of artificial neural networks and genetic algorithms theory, BP neural network applications in the process of facing network training a long time and easily into the local minimum value, hidden nodes is difficult to determine such shortcomings, in order to effectively overcome the difficulties BP network, Genetic Algorithm and BP organic integration of the network, so that they complement each other between enhance mutual capability, thus more effective to solve practical problems. Platform: |
Size: 558080 |
Author:罗旺 |
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Description: 利用遗传算法优化BP神经网络
即用遗传算法代替BP网络中的训练函数-The use of genetic algorithm to optimize BP neural network using genetic algorithms in place of BP network training function Platform: |
Size: 2048 |
Author:zhouye |
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Description: BP神经网络算法,采用遗传算法训练具有7个隶属函数的FNN控制器权值的程序等-BP neural network algorithm, using genetic algorithm training with seven membership function of the FNN controller weighting procedures Platform: |
Size: 5120 |
Author:肖平 |
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Description: 微粒群算法[PSO ] 是由Kennedy 和Eberhart等于1995 年开发的一种演化计算技术, 来源于对鸟群捕食过程的模拟。PSO同遗传算法类似，是一种基于叠代的优化工具，但与遗传算法使用遗传操作子进行优化不同，利用群体中各个体之间的“协作”与“竞争”关系，根据自身及其竞争者的飞行经验，调整自己的行为。同遗传算法比较，PSO的优势在于简单容易实现并且没有许多参数需要调整。目前已广泛应用于函数优化，神经网络训练，模糊系统控制以及其他遗传算法的应用领域。-Particle Swarm Optimization [PSO] are equal by Kennedy and Eberhart in 1995 developed an evolutionary computing technology, from preying on the birds of the simulation process. PSO with genetic algorithm is similar to an iterative optimization-based tool, but the use of genetic algorithms and genetic manipulation of different sub-optimize the use of groups between the various entities within the " collaboration" and " competitive" relationship, according to themselves and their competition the flying experience, adjust their behavior. Comparison with genetic algorithms, PSO has the advantage of being simple and easy and did not realize the need to adjust the parameters much. Has been widely applied to function optimization, neural network training, fuzzy system control, as well as other genetic algorithm applications. Platform: |
Size: 883712 |
Author:wzy |
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Description: 利用遗传算法优化BP神经网络权值和阈值，然后进行训练，利用训练好的模型进行预估。附实例-The use of genetic algorithm to optimize BP neural network weights and thresholds, and then training, training a good use of forecast models. Attached examples Platform: |
Size: 20480 |
Author:汲平 |
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Description: 用遗传算法优化bp神经网络的权值，并训练以及验证网络-Bp by using genetic algorithms to optimize the weights of neural networks, and network training and validation Platform: |
Size: 2048 |
Author:ruoshui |
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Description: 要建立一个有效的支持向量回归(SVR)模型，支持向量回归的3个参数c，y，占丛须预先设定。提出一种新型的遗传算
法一智能遗传算法(IGA)对支持向量回归进行参数调节，以达到寻找最优参数的目的，然后和支持向量回归结合得到一种新的
IGASVR模型，并应用于城市人口预测。最后，将提出的方法与标准SVR模型和BP神经网络模型进行比较，所得结果表明，该模
型训练速度快，并且有较高预测精度，是一种有效的人口预测方法。-To build an effective SVR model，SVR’8 parameters must be set carefully．This study proposes a novel approach，
known ag IGASVR。which searches for SVR’s optimal parameters using intelligent genetic algorithms，and then adopts the optimal
parameters to construct the SVR models．Finally we apply IGASVR tO forecast population．The experimental results demonstrates
that IGASVR are better than standard SVR and BP neural-network．IGASVR model is an effective approach which has faster
speed of training and higher precision． Platform: |
Size: 372736 |
Author:11 |
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Description: 用遗传算法来优化BP神经网络，进行网络训练，包含有实验数据-Using genetic algorithms to optimize the BP neural network, the network training, includes the experimental data Platform: |
Size: 17408 |
Author:李婷 |
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Description: 利用遗传算法来优化BP神经网络来对神经网络进行训练，以现场实际采集的数据为训练样本，使得训练后的网络可以用来预测下一卷铝板轧制的轧制力-Genetic algorithm to optimize BP neural network to train the neural network, the actual collection of data for on-site training sample, so that the network can be used to predict the next roll of aluminum plate rolling rolling force training Platform: |
Size: 3072 |
Author:yangkang |
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Description: 神经网络算法在图像阈值分割中的实现，包括遗传神经网络训练示例，神经网络分割示例，传统BP训练遗传BP训练等-Neural network algorithm for image thresholding in implementation, including genetic neural network training example, neural network segmentation example, the traditional genetic BP BP training training Platform: |
Size: 1024 |
Author:蔡小成 |
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Description: 采用遗传算法训练具有7个隶属函数的FNN控制器权值的一个好用程序,读自MATLAB工具箱的神经网络理论与应用程序源代码一书。-A useful training program using genetic algorithms with seven membership function FNN controller weights, read from MATLAB toolbox neural network theory and application source code book. Platform: |
Size: 3072 |
Author:angel |
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Description: 用MATLAB实现自适应神经模糊推理系统（ANFIS）结构训练。代码中，首先创建一个初始原ANFIS结构，然后采用遗传算法（GA）、粒子群优化（PSO）来训练ANFIS。此进化训练算法可用于解决非线性回归函数逼近问题。-Implementation of adaptive neural fuzzy inference system (ANFIS) based on MATLAB. Code, the first to create an initial original ANFIS structure, and then using the genetic algorithm (GA), particle swarm optimization (PSO) to train ANFIS. This evolutionary training algorithm can be used to solve the nonlinear regression function approximation problem. Platform: |
Size: 21504 |
Author:张贝 |
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Search - genetic neural training