Table of Contents
What is counter propagation?
CPN (COUNTERPROPAGATION NETWORK): They are multilayer network based on the combinations of the input, output, and clustering layers. The application of counterpropagation net are data compression, function approximation and pattern association.
What are counter propagation networks give its phases and basic design?
An example of a hybrid network which combine the features of two or more basic network designs. Proposed by Hecht-Nielsen in 1986. The hidden layer is a Kohonen network with unsupervised learning and the output layer is a Grossberg (outstar) layer fully connected to the hidden layer.
What consists of basic counter propagation?
What consist of a basic counterpropagation network? Explanation: Counterpropagation network consist of two feedforward network with a common hidden layer.
What type of learning is normally used to train the Outstar weights of a Counterpropagation network?
Fuzzy Competitive Learning
In this process, the weight connecting between the instar and outstar, that is, input-hidden and hidden-output layer, respectively, is adjusted by using Fuzzy Competitive Learning (FCL).
What is art in soft computing?
Adaptive resonance theory (ART) is a theory developed by Stephen Grossberg and Gail Carpenter on aspects of how the brain processes information. It describes a number of neural network models which use supervised and unsupervised learning methods, and address problems such as pattern recognition and prediction.
What are the parameters used in full CPN training?
Training Algorithm•The parameters used are:•x – Input training vector x=(x1,…,xi,…,xn)•y – Target output vector y=(y1,…,yk,…,ym)•zj – activation of cluster unit Zj. x* – Approximation to vector x. y* – Approximation to vector y. vij – weight from x input layer to Z-cluster layer.
What is the objective of backpropagation algorithm?
Explanation: The objective of backpropagation algorithm is to to develop learning algorithm for multilayer feedforward neural network, so that network can be trained to capture the mapping implicitly.
What is hamming network?
Hamming Network It is a single layer network. The inputs can be either binary {0, 1} of bipolar {-1, 1}. The weights of the net are calculated by the exemplar vectors. It is a fixed weight network which means the weights would remain the same even during training.
What is associative memory in neural network?
An associative memory is a content-addressable structure that maps specific input representations to specific output representations. It is a system that “associates” two patterns (X, Y) such that when one is encountered, the other can be recalled.
Why Hopfield model is necessary?
Thus, similar to the human brain, the Hopfield model has stability in pattern recognition. A Hopfield network is a single-layered and recurrent network in which the neurons are entirely connected, i.e., each neuron is associated with other neurons.
How do you explain backpropagation?
Backpropagation is used to adjust how accurately or precisely a neural network processes certain inputs. Backpropagation as a technique uses gradient descent: It calculates the gradient of the loss function at output, and distributes it back through the layers of a deep neural network.
What is Boltzmann algorithm?
Boltzmann Machine is a kind of recurrent neural network where the nodes make binary decisions and are present with certain biases. Several Boltzmann machines can be collaborated together to make even more sophisticated systems such as a deep belief network.
What is the difference between counter propagation and back propagation networks?
Counter propagation networks tend to be larger than back propagation networks. If a certain number of mappings are to be learned, the middle layer must have that many number of neurons. 1.
What are the different types of counterpropagation networks?
There are two types of counterpropagation net: 1. Full counterpropagation network 2. Forward-only counterpropagation network Full CPN efficiently represents a large number of vector pair x:y by adaptively constructing a look-up-table. The full CPN works best if the inverse function exists and is continuous.
How difficult is it to train a counter propagation network?
Training a counter propagation network has the same difficulty associated with training a Kohonen network. Counter propagation networks tend to be larger than back propagation networks. If a certain number of mappings are to be learned, the middle layer must have that many number of neurons.
What is full counterpropagation network?
Full counterpropagation network: Full CPN efficiently represents a large number of vector pair x:y by adaptively constructing a look-up-table. The full CPN works best if the inverse function exists and is continuous. The vector x and y propagate through the network in a counterflow manner to yield output vector x* and y*.