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What's Next in Neural Network Robotics

Robots designed with neural networks can monitor and analyze complex patterns of process variables to predict and correct pending production problems before they occur. Artificial Neural Networks (ANNs) may be trained in a supervised or unsupervised fashion. Supervised instruction is much more common. The network is usually presented with problems for which the desired outputs are known and grading the network's performance as network configurations are incrementally adjusted. For relatively elemental problems and minuscule networks, these adjustments may be relatively random "trial and error" varies. However, for intricate problems and immense networks, "trial and error" takes far too long. Mathematical study and optimization algorithms are used that facilitate the network propagate closer to desired outputs in a more efficient and timely manner. In contrast, with unsupervised learning, the network must self-organize without guidance from predefined right answers. Evobots join rudimentary, adaptive mechanisms with selective continuation to modify and improve their environmental functionality. Self-organizing evobots do not require a supervised practice period. This gives them bigger structural flexibility, but it furthermore makes them less predictable. The interaction between the neural network and ecology will potentially be supervised by a third-party to incline network development in the direction of desired outcomes. For organic neural networks, this can be done by instruction and modeling. The human brain is an organic neural network with approximately 1,000 billion neurons and dynamic connections. For inorganic neural networks, this will likely be done by priming the network with sizable datasets and specified data relationships. Inorganic neural networks are frequently labeled Artificial Neural Networks (ANNs). Whether through independent interactions with the biosphere or interactions influenced by a third-party, the forms of neural networks adapt in reaction to the natural stimuli that they encounter. In some respects, computers with neural networks program themselves. However, this is frequently not totally true. Generally the network learns from exposure to a set of education cases which, in turn, must be selected by an operator. Rather than completely "programming themselves," neural networks might be thought of as taking "general direction" from an operator, but exercising significant independence with respect to the details of how this general direction is followed. Click on images for info about biotech/neurotech T-shirts: Questions and suggestions concerning the site Isobot.com may be sent to: Isobot.com © 2006 by Isobot.com