FUNDAMENTAL CONCEPT OF ARTIFICIAL NETWORKS UNIT -2(Part1) FUNDAMENTAL CONCEPT OF ARTIFICIAL NETWORKS

Models of ANN :- Model of ANN are specified by the three basic entities namely:- (1) Model’s Synaptic Connection (2) Training or Learning Rules adopted for updating and adjusting the connection weights (3) Activation functions

(1)Model’s Synaptic Connection (A) Single Layer Feed Forward network (B) Multilayers Feed Forward Network (C) Single node with its own feedback (D) Single Layer recurrent network (E) Multilayer recurrent network The arrangement of neurons to form layers and the connection pattern formed within and between layers is called network architecture. Note:-Recurrent network are feed back network with closed loop, When output can be directed back as input to same or preceding layer nodes then it results in the formation of feedback networks

(2) Training or Learning Rules Supervised Learning - Providing the network with a series of sample inputs and comparing the output with the expected responses. Unsupervised Learning - Most similar input vector is assigned to the same output unit. Reinforcement Learning - Right answer is not provided but indication of whether ‘right’ or ‘wrong’ is provided. ANN main property is its capability to learn. Learning or training is a process by mean of which a neural network adapts itself to a stimulus by making proper parameters adjustment resulting in production of desired response. Supervised learning-performed with the help of teacher and teacher is required for error minimization. Unsupervised learning-performed without the help of teacher. Reinforcement learning-It is similar to supervised learning.learning is based on the critic information is called reinforcement learning and feedback sends is reinforcement signal. Reinforcement is also known as learning with critics as opposed to learning with a teacher, with indicate supervised learning.

(3) Activation functions: (A) Identity (B) Binary step (C) Bipolar step (D) Binary sigmoidal (E) Bipolar sigmoidal (F) Ramp Activation function is applied over net input to calculate net output. Each input into the neuron has its own weight associated with it. A weight is simply a floating point number and it's these we adjust when we eventually come to train the network. The weights in most neural nets can be both negative and positive, therefore providing excitory or inhibitory influences to each input. As each input enters the nucleus it's multiplied by its weight. The nucleus then sums all these new input values which gives us the activation (again a floating point number which can be negative or positive). If the activation is greater than a threshold value - lets use the number 1 as an example - the neuron outputs a signal. If the activation is less than 1 the neuron outputs zero. This is typically called a step function

Questions

Important terminologies of ANN 1)Threshold

2)Learning rate 3) Momentum factor

Basic notations

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