Artificial neural networks (ANNs) are computational paradigms which implement simplified models of their biological counterparts, biological neural networks. Biological neural networks are the local assemblages of neurons and their dendritic connections that form the (human) brain. Accordingly, artificial neural networks are characterised by:
- Local processing in artificial neurons (processing elements)
- Massively parallel processing, implemented by rich connection pattern between processing elements
- The ability to acquire knowledge via learning from experience
- Knowledge storage in distributed memory, the synaptic processing elements connections
The attempt of implementing neural networks for brain like computations like patterns recognition, decisions making, motor control and many others is made possible by the advent of large scale computers in the late 1950's. Indeed artificial neural networks can be viewed as a major new approach to computational methodology since the introduction of digital computers.
Although the initial intent of artificial neural networks was to explore and reproduce human information processing tasks such as speech, vision, and knowledge processing, artificial neural networks also demonstrated their superior capability for classification and function approximation problems. This has great potential for solving complex problems such as systems control, data compression, optimization problems, pattern recognition, and system identification.
Artificial neural networks were originally developed as tools for the exploration and reproduction of human information processing tasks such as speech, vision, touch, knowledge processing and motor control. Today, most research is directed towards the development of artificial neural networks for applications such as data compression, optimisation, pattern matching, system modeling, function approximation, and control. One of the application areas to which artificial neural networks are applied is flight control. Artificial neural networks give control systems a variety of advanced capabilities.
Since artificial neural networks are highly parallel systems, conventional computers are unsuited for neural network algorithms. Special purpose computational hardware has been constructed to efficiently implement artificial neural networks. Accurate Automation has developed a Neural Network Processor. This hardware will allow us to run even the most complex neural networks in real time. The neural network processor is capable of multiprocessor operation in Multiple Instruction Multiple Data (MIMD) fashion. It is the most advanced digital neural network hardware in existence. Each neural network processor system is capable of implementing 8000 neurons with 32,000 interconnections per processor. The computational capability of a single processor 140 million connections per second. An 8 processor neural network processor would be capable of over one billion connections per second. The neural network processor architecture is extremely flexible and any neuron is capable of interconnecting with other neuron in the system.
Conventional computers rely on programs that solve a problem using a predetermined series of steps, called algorithms. These programs are controlled by a single, complex central processing unit, and store information at specific locations in memory. Artificial neural networks use highly distributed representations and transformations that operate in parallel, have distributed control through many highly interconnected neurons, and store their information in variable strength connections called synapses.
There are many different ways in which people refer to the same type of neural networks technology. Neural networks are described as connectionist systems, because of the connections between individual processing nodes. They are sometimes called adaptive systems, because the values of these connections can change so that the neural network performs more effectively. They are also sometimes called parallel distributed processing systems, which emphasise the way in which the many nodes or neurons in a neural network operate in parallel. The theory that inspires neural network systems is drawn from many disciplines, primarily from neuroscience, engineering, and computer science, but also from psychology, mathematics, physics, and linguistics. These sciences are working toward the common goal of building intelligent systems.
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