Introduction To Neural Networks Using Matlab 6 0 S N Sivanandam Sumathi Deepa

neural networks were first introduced by the russian physiologist, ivan pavlov in the 19th century. pavlov is credited for building the first artificial neural network (ann), a rudimentary form of artificial intelligence which uses a set of weighted connections, usually referred to as weights, to form a computational structure or model. in the late 20th century, neural networks were used by large companies such as ibm, ford and motorola to understand and improve the performance of their products and systems. most of the current research in neural networks is now focused on learning, in which neural networks are designed to acquire knowledge from experience.

introduction to neural networks using matlab 6 0 s n sivanandam sumathi deepa

a neural network is an artificial intelligence model that is inspired by the structure and function of biological neural networks. the basic unit of a neural network is the neuron. each neuron is connected to other neurons via weighted connections (or synapses). neural networks can be categorized as feed-forward, feedback and recurrent. feed-forward neural networks or purely feedforward networks have no feedback or memory. in contrast, feedback neural networks or recurrent neural networks have a feedback loop or memory in them. in a feed-forward neural network, the signals travel from input to output in a single direction, from input to output, through the network. the weight value is a measure of the strength of the connection between two neurons. feed-forward neural networks are most often used in connection with supervised learning and/or reinforcement learning, in which the output of the network is a function of the input. feed-forward neural networks are found in problems such as handwritten digit recognition, speech recognition, and understanding natural language. feed-forward neural networks can also be used to solve prediction problems. a recurrent neural network is a neural network which has connections between neurons in multiple cycles or loops. neural networks with feedback connections are most commonly used in connection with unsupervised learning and/or reinforcement learning. recurrent neural networks can be used for tasks such as handwriting recognition, speaker recognition, word prediction, and word compression. neural networks can be grouped into broad categories such as local and global. local neural networks have neurons which are very close to each other. global neural networks have neurons which are not very close to each other. applications such as speech recognition, handwriting recognition, and face recognition involve neural networks which are local in nature. applications such as speaker recognition, translation, and natural language processing involve neural networks which are global in nature. a supervised learning neural network takes data as input and produces output. in supervised learning, the output of the network is a function of the input. the examples of supervised learning neural networks are neural classifiers, neural pattern classifiers, and neural classifiers. an unsupervised learning neural network is one that has no input or output. the goal of an unsupervised learning neural network is to find a pattern or structure in the input data without any guidance. the examples of unsupervised learning neural networks are the denoising neural networks, denoising auto-encoders, unsupervised feature discovery, and unsupervised feature extraction. the purpose of the unsupervised feature discovery neural network is to find a set of features or patterns in a set of data that represent the data. a reinforcement learning neural network is a neural network that learns from experience, with experience consisting of rewards and punishments. the reward and punishment is a measure of how successful the neural network is in a task. in reinforcement learning, the neural network receives feedback from the environment and modifies its connection weights to improve its performance. the most common method for reinforcement learning is the temporal difference method.

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