Artificial Neural Networks (ANNs) are Intelligent and Non Parametric Mathematical Models - IT/Computer Science Assignment Help

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Artificial neural networks (ANNs) are intelligent and non-parametric mathematical models inspired by the biological nervous system. In the last three decades, ANNs have been widely investigated and applied to classification, pattern recognition, regression, and forecasting problems Schmidhuber (2015)(Schmidhuber 2015; Chatterjee et al 2016; Braik et al 2008; Linggard et al 2012; ?; Rezaeianzadeh et al. 2014). The efficiency of ANNs is highly affected by its learning process. For multi-layer perceptron (MLP) neural networks, which are the most common and applied ANNs, there are two main categories of supervised training methods: gradient-based and stochastic methods. The back-propagation algorithm and its variants Zhang et al. (2015)(Wang et al. 2015; Kim and Jung 2015) are considered as standard examples of gradient-based methods and the most popular between researchers. However, there are three main disadvantages in the gradient-based methods: tendency to be trapped in local minima, slow convergence, and high dependency on the initial parameters Faris et al. (2016)(Faris et al 2016; Mirjalili 2015; Anna 2012).

Evolutionary algorithms were deployed in the supervised learning of MLP networks in three different main schemes: automatic design of the network structure, optimizing the connection weights and biases of the network, and evolving the learning rules Yu et al. (2008)(Jianbo et al. 2008 ). It is important to mention here that simultaneous optimization of the structure and weights of the MLP network can drastically increase the number of parameters, so it can be considered a large-scale optimization problem (Karaboga et al 2007 ). In this work, we focus only on optimizing the connection weights and the biases in the MLP network.

Important Definitions Discussed Biases : A bias vector is an additional set of weights in a neural network that require no input, and this it corresponds to the output of an artificial neural network when it has zero input. Bias represents an extra neuron included with each pre-output layer and stores the value of “1,” for each action. Bias units aren’t tied to any previous layer in the network, so they don’t represent any form of activity, but are treated the same as any other weight. Artificial Neural Network: The data structures and functionality of neural nets are designed to simulate associative memory. Neural nets learn by processing examples, each of which contains a known "input" and "result," forming probabilityweighted associations between the two, which are stored within the data structure of the net itself. (The "input" here is more accurately called an input set, since it is generally comprised of multiple independent variables, rather than a single value.) Thus, the "learning" of a neural net from a given example is the difference in the state of the net before and after processing the example.

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