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ARTIFICIAL NEURAL NETWORKSARTIFICIAL NEURAL NETWORKS Olexander Voznyuk National Aviation University Abstract In modern software implementations of artificial neural networks, the approach inspired by biology has been largely abandoned for a more practical approach based on statistics and signal processing. In some of these systems, neural networks or parts of neural networks form components in larger systems that combine both adaptive and non-adaptive elements. Historically, the use of neural networks models marked a paradigm shift in the late eighties from high-level artificial intelligence, characterized by expert systems with knowledge embodied in if-then rules, to low-level machine learning, characterized by knowledge embodied in the parameters of a dynamical system. ARTIFICIAL NEURAL NETWORKS An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. The key element of this paradigm is the novel structure of the information processing system. It is composed of a large number of highly interconnected processing elements (neurons) working in unison to solve specific problems. ANNs, like people, learn by example. An ANN is configured for a specific application, such as pattern recognition or data classification, through a learning process. Learning in biological systems involves adjustments to the synaptic connections that exist between the neurons. This is true of ANNs as well. The word network in the term 'artificial neural network' refers to the inter–connections between the neurons in the different layers of each system. An example system has three layers. The first layer has input neurons which send data via synapses to the second layer of neurons, and then via more synapses to the third layer of output neurons. More complex systems will have more layers of neurons with some having increased layers of input neurons and output neurons. The synapses store parameters called "weights" that manipulate the data in the calculations. Introduction Neural networks, with their remarkable ability to derive meaning from complicated or imprecise data, can be used to extract patterns and detect trends that are too complex to be noticed by either humans or other computer techniques. A trained neural network can be thought of as an "expert" in the category of information it has been given to analyse. This expert can then be used to provide projections given new situations of interest and answer "what if" questions.
Other advantages include: - Adaptive learning: An ability to learn how to do tasks based on the data given for training or initial experience. - Self-Organization: An ANN can create its own organization or representation of the information it receives during learning time. - Real Time Operation: ANN computations may be carried out in parallel, and special hardware devices are being designed and manufactured which take advantage of this capability. - Fault Tolerance via Redundant Information Coding: Partial destruction of a network leads to the corresponding degradation of performance. However, some network capabilities may be retained even with major network damage. An ANN is typically defined by three types of parameters: 1. The interconnection pattern between the different layers of neurons 2. The learning process for updating the weights of the interconnections 3. The activation function that converts a neuron's weighted input to its output activation. Mathematically, a neuron's network function
This figure depicts such a decomposition of The first view is the functional view: the input The second view is the probabilistic view: the random variable The two views are largely equivalent. In either case, for this particular network architecture, the components of individual layers are independent of each other (e.g., the components of Artificial Learning What has attracted the most interest in neural networks is the possibility of learning. Given a specific task to solve, and a class of functions This entails defining a cost function The cost function For applications where the solution is dependent on some data, the cost must necessarily be a function of the observations; otherwise we would not be modeling anything related to the data. It is frequently defined as a statistic to which only approximations can be made. As a simple example, consider the problem of finding the model When Learning paradigms There are three major learning paradigms, each corresponding to a particular abstract learning task. These are supervised learning, unsupervised learning and reinforcement learning. Date: 2015-12-18; view: 823
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