This is a demo of Single Layer Neural Network (NN) with supervised learning. Single Layer Neural Network can solve linear problem or it can be used to linearly separate set of data. The example shown below uses a Single layer Neural Network with two inputs, one biasing input neuron and one output neuron. Neurons are connected through weights that are trained using delta rule. We apply random inputs and train with functions OR, AND and XOR in three different NNs. The net's learns OR and AND successfully where as XOR error does not converge. XOR being a non linear function of inputs, it cannot be learned by single layer NN. We need Multi Layer Neural Network to train non linear functions like XOR.
