1. How to determine the number of network layers and the number of neurons in each layer in neural network control
What neural network are you using? If it is an RBF neural network, there are only 3 layers, the input layer, the hidden layer and the output layer. There are K-means, ROLS and other algorithms to determine the number of neurons.
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2. How to determine the number of hidden layers and neurons in the neural network?
What neural network are you using? If it is an RBF neural network, there are only 3 layers, the input layer, the hidden layer and the output layer. The neural network self-adjusts the number of layers and the number of neurons . There are K-means, ROLS and other algorithms to determine the number of neurons.
3. How to determine the number of output neurons in the neural network
If it is an RBF neural network, there are only 3 layers, the input layer, the hidden layer and the output layer. There are K-means, ROLS and other algorithms to determine the number of neurons. There is no mature theorem that can determine the number of neurons in each layer of neurons and how many layers of networks it contains. Most of them rely on experience, but a 3-layer network can approximate any nonlinear network, and the more the number of neurons, the more the approximation The better the effect.
There are two kinds of neural networks, one is biological neural network and the other is artificial neural network.
Biological neural network: generally refers to the network composed of biological brain neurons, cells, contacts, etc., which are used to generate biological consciousness and help biological thinking and actions.
Artificial Neural Networks (ANNs for short), also referred to as neural networks (NNs) or connection models (Connection Model), is a kind of algorithmic mathematics that imitates the behavior characteristics of animal neural networks and performs distributed parallel information processing. Model. This kind of network depends on the complexity of the system, and achieves the purpose of processing information by adjusting the interconnection relationship between a large number of internal nodes.
Artificial neural network: It is a mathematical model that uses a structure similar to the synaptic connection of the brain for information processing. In engineering and academia, it is often referred to directly as "neural network" or neural network.
4. How to set the number of neurons in the neural network in matlab? I want to set 5 neurons!
net=newff([x,y],[a1,a2,...,ak],{f1,f2,...,fk}) x and
y are column vectors respectively, storing the minimum value and ude of each sample book maximum value. [a1,a2,...,ak] is a row vector, which is the number of nodes in each layer of the input neural network, which is the problem of your title. k refers to the number of hidden layers of neurons. {f1,f2,...,fk} input variable is a unit array, corresponding to the transfer function type of neurons in each layer.
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5. The number of neurons in BP neural network for help
You are using the new version of the newff function. You don’t need to manually set the number of input and output neurons, you only need to set the hidden layer neurons. From your HideLayerNode=[17 7]; it can be seen that you are a double hidden layer network, the first hidden layer is 17 neurons, and the second hidden layer is 7 neurons.
net = newff(P,T,[S1 S2...S(Nl)],{TF1 TF2...TFNl}, BTF,BLF,PF,IPF,OPF,DDF) Size of ith layer, for N-
1 layers, default = [ ].
(Output layer size SN is determined from T.)
The number of output layer neurons is determined by the sample dimension.
6. What is the difference between the number of neurons in a certain layer of the BP neural network and the number of nodes in that layer?
The number of neurons and the number of nodes in a certain layer mean the same thing. According to your assumption: Although it is a 3-layer neural network, it is called a two-layer BP network, because the input layer is generally not counted as one layer. According to your assumption, n should take 2, s1 is the number of nodes in the hidden layer, the selected formula is the
formula proposed by Hornik, the value range of s1 can be calculated, then choose the appropriate position by yourself, s2 is the number of nodes in your output layer , which is the output dimension.
7. Ask about neural networks
The number of neurons in the hidden layer of the neural network is specified by itself. At present, there is no widely useful formula to determine the number of neurons, and it is generally specified based on experience, or obtained by trial and error. I'm also confused by your later question, and I don't quite understand it.
You can go to some matlab forums to ask questions, and there will be many professional people answering your questions. I think the ilovematlab forum is good, you can ask.