搜索资源列表
BP-LM
- 一种利用神经网络计算L-M算法的程序。里面还有其他的算法可以参考-A neural network algorithm for calculating the LM procedure. There are other algorithms which can refer to
bp
- bp神经网络算法及相关文章，有一定的使用价值，用过就知道-bp neural network algorithm and related articles, a certain degree of value, used to know
BP
- 神经网络BP算法,c语言编写的,通用可移植!-BP neural network algorithm, c language, general-purpose portable!
BP
- BP 算法的实现，分别包含了C++，Matlab，Forturn等几种语言的实现方法
BP
- bp神经网络程序，一个简单的训练算法和样本-bp neural network program, a simple algorithm and the training samples
BP
- BP neural network加动量项的算法-BP neural network algorithm increases the momentum of
BP
- 人工神经网络中著名的BP算法的实现，在VC下可以直接运行-Artificial Neural Networks in the well-known BP algorithm, the VC can be directly run
BP
- 用C实现的BP算法，大家可以参考一下，多多交流-BP algorithm is implemented using C, we can reference, more exchanges
BP
- 人工神经网络BP算法的MATLAB实现--源代码-Artificial neural network BP algorithm MATLAB implementation- the source code
1
- matlab应用于神经网络：利用BP算法及Sigmoid函数，研究函数的逼近问题。-matlab applied to neural networks: the use of BP algorithm and Sigmoid function is to study the function approximation problem.
work
- GA+BP遗传算法与神经网络结合，与单纯的Bp算法作为比较-GA+BP
BP
- 比较经典的一种BP算法源程序，神经网络的方面的东西。C++编写的-Comparison of the classical BP algorithm is a kind of source, neural networks of things. Written in C++
GA-bp
- 基于遗传算法的bp神经网络优化程序，经测试好用，有很好的应用价值-Bp based on genetic algorithm neural network optimization program, tested easy to use, there is a good value
bp
- 一个例子，使用的是BP神经网络代码，采用的基本下降梯度算法-An example, using BP neural network code, using the basic down gradient algorithm
bp
- 利用BP算法实现加权系数的优化，实现误差最小-BP algorithm using the weighted coefficient is optimized to achieve the smallest error
example_bpnetronetwork
- BP算法例子：用一个五层的神经网络去逼近函数-example_bpnetronetwork_5layers
bp
- 一种用于信息融合的bp算法（vc),初学者，里面有不对的地方，大家注意或者仔细看看啊-Bp one for information fusion algorithm (vc)
BP
- 基于C++程序的神经网络Bp算法-Based on C++ programs Bp neural network algorithm for
BP
- bp是MATLAB应用中的预测算法，含有例题-BP and MATLAB
BP~
- BP神经网络算法，可以调节隐层，输入输出等-BP neural network algorithm, can be adjusted hidden layer, input and output, etc.