neural network (redirected from Present challenges in neural Networks)

neural network or neural computing, computer architecture modeled upon the human brain's interconnected system of neurons. Neural networks imitate the brain's ability to sort out patterns and learn from trial and error, discerning and extracting the relationships that underlie the data with which it is presented. Most neural networks are software simulations run on conventional computers. In neural computers, transistor circuits serve as the neurons and variable resistors act as the interconnection between axons and dendrites (see nervous system). A neural network on an integrated circuit, with 1,024 silicon "neurons," has also been developed. Each neuron in the network has one or more inputs and produces an output; each input has a weighting factor, which modifies the value entering the neuron. The neuron mathematically manipulates the inputs, and outputs the result. The neural network is simply neurons joined together, with the output from one neuron becoming input to others until the final output is reached. The network learns when examples (with known results) are presented to it; the weighting factors are adjusted—either through human intervention or by a programmed algorithm—to bring the final output closer to the known result.

Neural networks are good at providing very fast, very close approximations of the correct answer. Although they are not as well suited as conventional computers for performing mathematical calculations or moving and comparing alphabetic characters, neural networks excel at recognizing shapes or patterns, learning from experience, or sorting relevant data from irrelevant. Their applications can be categorized into classification, recognition and identification, assessment, monitoring and control, and forecasting and prediction. Among the tasks for which they are well suited are handwriting recognition, foreign language translation, process control, financial forecasting, medical data interpretation, artificial intelligence research, and parallel processing implementations of conventional processing tasks. In an ironic reversal, neural networks are being used to model disorders of the brain in an effort to discover better therapeutic strategies.

Bibliography

See Y. Burnod, An Adaptive Neural Network: The Cerebral Cortex (1990); J. S. Judd, Neural Network Design and the Complexity of Learning (1990); S. I. Gallant, Neural Network Learning and Expert Systems (1993); L. Medsker, Hybrid Neural Network and Expert Systems (1994); R. L. Harvey, Neural Network Principles (1994).

---

neural network

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.

---

A modeling technique based on the observed behavior of biological neurons and used to mimic the performance of a system. It consists of a set of elements that start out connected in a random pattern, and, based upon operational feedback, are molded into the pattern required to generate the required results. It is used in applications such as robotics, diagnosing, forecasting, image processing and pattern recognition.

---

neural network - artificial neural network