Neural network state observer

It has been observed that the use of protein tertiary structural class improved the accuracy for a 2-state secondary structure prediction (Kneller et al., 1990). A modular network architecture was proposed using separate networks (i.e., a- or P-type network) for classification of different secondary structures (Sasagawa Tajima, 1993). Recently, Chandonia Karplus (1995) trained a pair of neural networks to predict the protein secondary structure and the structural class respectively. Using predicted class information, the secondary structure prediction network realized a small increase in accuracy. 

While many of these points are valid, there are often solutions which help alleviate any problems. As just explained, the use of d5mamic features helps greatly with the problems of observation independence and discrete states. As we shall see the linearity issue is potentially more of a problem in speech s mthesis. Models such as neural networks which perform classification directly have been proposed [375] and have produced reasonable results. More recently, discriminative training has become the norm in ASR [495], [360] where HMMs as described are used, but where their parameters are trained to maximise discrimination, not data likelihood. 

Natural muscles are controlled by neurons and network of neurons. We can imagine artificial neurons and network of artificial neurons as well. Artificial muscles with motor proteins are studied and attract attention[79]. One direction is to develop deformable machine with real motor proteins, actins and myosins, and neurons. Another direction is to develop neural network software to control distributed artificial muscles. The author has been developing open brain simulator which can emulate the activities of human nervous system for estimating internal state of human through external observation [231]. Such software is also applicable to control artificial muscle systems, which is implemented on the personal robots and humanoid robots in the future. 

Fig. 2 Neural activation in responses to smoking (vs, neutral) cues, showing the effects of expectancy to smoke and by abstinence states. Overall, the network of brain regions recruited by smoking-related cues was affected only slightly by abstinence, but was affected dramatically by expectancy, with greater activation observed when participants believed they would be allowed to smoke after the scan (top) (McBride et al. 2006)...

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