A combination of Neural networks genetic algorithms and conventional computing in Multimedia and Gaming industry has never been attempted before. Existing technologies operate on fixed databases and manipulate limited data that is bound by some logical algorithm. Even many of the AI based products don't implement intelligent thinking. Using a combination of AI,ANN genetic algorithms & conventional computers intelligent gaming characters and intelligent Virtual Stuntmen can be created that simulate a realistic atmosphere. Even though the technical implementation is difficult, the future is promising. This paper looks at the possibilities of such a complex system and its different components. Artificial Neural Networks are a very different paradigm of computing. Artificial Neural Networks are self-learning mechanisms, which don't require the traditional skills of a programmer. A few of these neuron-based structures, paradigms actually, are being used commercially.
[...] This output is then input into other processing elements, or to an outside connection, as dictated by the structure of the network. Neural Networks have a large number of Artificial neurons that have massive interaction capabilities spread over ,generally layers: Fig: Layers Of Neural Net Decision making: Firing rules The firing rule is an important concept in neural networks and accounts for their high flexibility. A firing rule determines whether a neuron should fire for any input pattern. It relates to all the input patterns, not only the ones on which the node was trained. [...]
[...] This training phase can consume a lot of time and is considered complete when the neural network reaches a user defined performance level. This level signifies that the network has achieved the desired statistical accuracy as it produces the required outputs for a given sequence of inputs. When no further learning is necessary, the weights are typically frozen for the application. Some network types allow continual training, at a much slower rate, while in operation. This helps a network to adapt to gradually changing conditions. [...]
[...] Its actions in different frames are a result of inputs given to it using Neural Networks and Genetic algorithms. In gaming too same technology can be used to create intelligent and more realistic characters. A neural network with an electrode grid can be used. Similar technology is already in use. A neural network that has the power to rearrange itself and respond according to the environment it is connected to is being used to simulate a F-22 fighter jet flight. [...]
[...] Thus the firing rule requires that the neuron should not fire when the input is 001 On the other hand is equally distant from two taught patterns that have different outputs and thus the output stays undefined So the adapted table will be : The difference between the two truth tables is called the generalization of the neuron. Therefore the firing rule gives the neuron a sense of similarity and enables it to respond “sensibly” to patterns not seen during its training. [...]
[...] This covering has gaps at regular intervals called Nodes of Ranvier. When a stimulus is applied to a neuron, a small electric potential of about 0.055 volts is generated. This is called Nerve Impulse or Action Potential. This impulse travels along the axon and to another organ or a neuron from there. The key element in Artificial Neural Networks is the novel structure of the information processing system which is composed of a large number of highly interconnected processing elements (neurons) working in unison to solve specific problems. [...]
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