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Adaptive neural networks can auto-change their models to find optimal network architecture. Finding optimal architecture is finding how many layers will be necessary for the neural networks to operate accurately. Structural adaptation is done with the help of three model selection techniques. The first technique performs a search through all the previously available architectures and finds the best-suited model. The second technique starts with a big and complex model and then simplifies it until optimal architecture is found. The third technique begins with a small model and then evolves itself as the learning increases. SEPA (Structure Evolution and Parameter Adaptation) algorithm, cascading algorithm, and constructive algorithm are some of the algorithms that are used to provide structural adaptability to artificial neural networks.
Functional adaptation is adapting the slope of activation functions of neural networks to reduce errors in outputs. Activation functions are mathematical functions that determine whether a neuron in the neural network should be fired or not. Activation functions do this by determining whether the input of the neuron will be useful to make predictions. They add non-linearity to the output of neural networks, making them capable of learning and performing complex tasks. Without activation functions, neural networks will be like linear regression models.
Parameter adaptation is adapting to changing input data functions, i.e., weights and biases while training. Every input in neural networks is associated with weights. Weights show what impact an input will have on the output. Greater the weight more is the impact of an input. And bias is a constant used to adjust the output along with the sum of weights so that the model can best fit the given data. If a neural network is adaptable to parameters, then the weights of the network can be changed while training according to a given problem. And, with the help of parameter adaptation, neural networks can get knowledge from new weight inputs without losing knowledge gained from previous inputs with minimum loss in accuracy. Algorithms like swarm optimization, genetic algorithm, and back-propagation algorithm can be used to provide parameter adaptability to adaptive neural networks.
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