See W. Stallings, ed., Advances in Local and Metropolitan Area Networks (1994); F. Halsall, Data Communications, Computer Networks, and Open Systems (4th ed. 1996); R. Cahn, Wide Area Network Design: Concepts and Tools for Optimization (1998); T. Parnell and C. Null, Network Administrator's Reference (1999).
Type of parallel computation in which computing elements are modeled on the network of neurons that constitute animal nervous systems. This model, intended to simulate the way the brain processes information, enables the computer to “learn” to a certain degree. A neural network typically consists of a number of interconnected processors, or nodes. Each handles a designated sphere of knowledge, and has several inputs and one output to the network. Based on the inputs it gets, a node can “learn” about the relationships between sets of data, sometimes using the principles of fuzzy logic. For example, a backgammon program can store and grade results from moves in a game; in the next game, it can play a move based on its stored result and can regrade the stored result if the move is unsuccessful. Neural networks have been used in pattern recognition, speech analysis, oil exploration, weather prediction, and the modeling of thinking and consciousness.
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Two or more computers and peripheral equipment (e.g., printers) that are connected with one another for the purpose of exchanging data electronically. Two basic network types are local area networks (LANs) and wide-area networks. Wide-area networks connect computers and smaller networks to larger networks over greater geographical areas, including different continents. Communications may occur over cables, fibre optics, or satellites, but most computer users access the network with a modem, using telephone lines. The largest wide-area network is the Internet. In the 1990s the World Wide Web was introduced and became the most popular way to access other Internet sites.
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