Above are papers related to the following paper: [Paper_ID] Paper #390693. 	[Title] NETWORKS WITH REAL WEIGHTS: ANALOG COMPUTATIONAL COMPLEXITY In contrast to classical computational models, the models [Abstract] Report SYCON-92-05 ABSTRACT We pursue a particular approach to analog computation, based on dynamical systems of the type used in neural networks research. Our systems have a fixed structure, invariant in time, corresponding to an unchanging number of "neurons". If allowed exponential time for computation, they turn out to have unbounded power. However, under polynomial-time constraints there are limits on their capabilities, though being more powerful than Turing Machines. (A similar but more restricted model was shown to be polynomial-time equivalent to classical digital computation in the previous work [17].) Moreover, there is a precise correspondence between nets and standard non-uniform circuits with equivalent resources, and as a consequence one has lower bound constraints on what they can compute. This relationship is perhaps surprising since our analog devices do not change in any manner with input size. We note that these networks are not likely to solve polynomially NP-hard problems, as the equality "p = np " in our model implies the almost complete collapse of the standard polynomial hierarchy. Please summarise all the given information.

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Paper #390693 has the title `NETWORKS WITH REAL WEIGHTS: ANALOG COMPUTATIONAL COMPLEXITY In contrast to classical computational models, the models
` and the abstract `Report SYCON-92-05 ABSTRACT We pursue a particular approach to analog computation, based on dynamical systems of the type used in neural networks research. Our systems have a fixed structure, invariant in time, corresponding to an unchanging number of "neurons". If allowed exponential time for computation, they turn out to have unbounded power. However, under polynomial-time constraints there are limits on their capabilities, though being more powerful than Turing Machines. (A similar but more restricted model was shown to be polynomial-time equivalent to classical digital computation in the previous work [17].) Moreover, there is a precise correspondence between nets and standard non-uniform circuits with equivalent resources, and as a consequence one has lower bound constraints on what they can compute. This relationship is perhaps surprising since our analog devices do not change in any manner with input size. We note that these networks are not likely to solve polynomially NP-hard problems, as the equality "p = np " in our model implies the almost complete collapse of the standard polynomial hierarchy. 
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Question: Which category should Paper #390693 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #390693 has the title `NETWORKS WITH REAL WEIGHTS: ANALOG COMPUTATIONAL COMPLEXITY In contrast to classical computational models, the models
` and the abstract `Report SYCON-92-05 ABSTRACT We pursue a particular approach to analog computation, based on dynamical systems of the type used in neural networks research. Our systems have a fixed structure, invariant in time, corresponding to an unchanging number of "neurons". If allowed exponential time for computation, they turn out to have unbounded power. However, under polynomial-time constraints there are limits on their capabilities, though being more powerful than Turing Machines. (A similar but more restricted model was shown to be polynomial-time equivalent to classical digital computation in the previous work [17].) Moreover, there is a precise correspondence between nets and standard non-uniform circuits with equivalent resources, and as a consequence one has lower bound constraints on what they can compute. This relationship is perhaps surprising since our analog devices do not change in any manner with input size. We note that these networks are not likely to solve polynomially NP-hard problems, as the equality "p = np " in our model implies the almost complete collapse of the standard polynomial hierarchy. 
`.
Question: Which category should Paper #390693 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].

This paper should be classified into: Neural_Networks