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Articles 1 - 7 of 7
Full-Text Articles in Physical Sciences and Mathematics
Genetic Algorithms For Soft Decision Decoding Of Linear Block Codes, Harpal Maini, Kishan Mehrotra, Chilukuri K. Mohan, Sanjay Ranka
Genetic Algorithms For Soft Decision Decoding Of Linear Block Codes, Harpal Maini, Kishan Mehrotra, Chilukuri K. Mohan, Sanjay Ranka
Electrical Engineering and Computer Science - Technical Reports
Soft-decision decoding is an NP-hard problem of great interest to developers of communication systems. We show that this problem is equivalent to the problem of optimizing Walsh polynomials. We present genetic algorithms for soft-decision decoding of binary linear block codes and compare the performance with various other decoding algorithms. Simulation results show that our algorithms achieve bit-error-probabilities as low as 0.00183 for a [104, 52] code with a low signal-to-noise ratio of 2.5 dB, exploring only 30,000 codewords, whereas the search space contains 4.5 x 1015 codewords. We define a new crossover operator that exploits domain-specific information and compare it …
Parametricity And Local Variables, Peter W. O'Hearn, R. D. Tennent
Parametricity And Local Variables, Peter W. O'Hearn, R. D. Tennent
Electrical Engineering and Computer Science - Technical Reports
We propose that the phenomenon of local state may be understood in terms of Strachey's concept of parametric (i.e., uniform) polymorphism. The intuitive basis for our proposal is the following analogy: a non-local procedure is independent of locally-declared variables in the same way that a parametrically polymorphic function is independent of types to which it is instantiated. A connection between parametricity and representational abstraction was first suggested by J. C. Reynolds. Reynolds used logical relations to formalize this connection in languages with type variables and user-defined types. We use relational parametricity to construct a model for an Algol-like language in …
On Inverse Sigmoid Functions, Anil Ravindran Menon, Kishan Mehrotra, Chilukuri K. Mohan, Sanjay Ranka
On Inverse Sigmoid Functions, Anil Ravindran Menon, Kishan Mehrotra, Chilukuri K. Mohan, Sanjay Ranka
Electrical Engineering and Computer Science - Technical Reports
Networks with sigmoid node functions have been shown to be universal approximators, and can use straightforward implementations of learning algorithms. Mathematically, what is common to different sigmoid functions used by different researchers? We establish a common representation of inverse sigmoid functions in terms of the Guass Hypergeometric function, generalizing different node function formulations. We also show that the continuous Hopfield network equation can be transformed into a Legendre differential equation, without assuming the specific form of the node function; this establishes a link between Hopfield nets and the method of function approximation using Legendre polynomials
Putting Humpty-Dumpty Together Again: Reconstructing Functions From Their Projections., Anil Ravindran Menon, Kishan Mehrotra, Chilukuri K. Mohan, Sanjay Ranka
Putting Humpty-Dumpty Together Again: Reconstructing Functions From Their Projections., Anil Ravindran Menon, Kishan Mehrotra, Chilukuri K. Mohan, Sanjay Ranka
Electrical Engineering and Computer Science - Technical Reports
We present a problem decomposition approach to reduce neural net training times. The basic idea is to train neural nets in parallel on marginal distributions obtained from the original distribution (via projection), and then reconstruct the original table from the marginals (via a procedure similar to the join operator in database theory). A function is said to be reconstructible, if it may be recovered without error from its projections. Most distributions are non-reconstructible. The main result of this paper is the Reconstruction theorem, which enables non-reconstructible functions to be expressed in terms of reconstructible ones, and thus facilitates the application …
A Generalization Of The Trie Data Structure, Richard H. Connelly, F. Lockwood Morris
A Generalization Of The Trie Data Structure, Richard H. Connelly, F. Lockwood Morris
Electrical Engineering and Computer Science - Technical Reports
Tries, a form of string-indexed look-up structure, are generalized to permit indexing by terms built according to an arbitrary signature. The construction is parametric with respect to the type of data to be stored as values; this is essential, because the recursion which defines tries appeals from one value type to others. "Trie" (for any fixed signature) is then a functor, and the corresponding look-up function is a natural isomorphism. The trie functor is in principle definable by the "initial fixed point" semantics of Smyth and Plotkin. We simplify the construction, however, by introducing the "category-cpo", a class of category …
Genetic Algorithms For Stochastic Flow Shop No Wait Scheduling, Harpal Maini, Ubirajara R. Ferreira
Genetic Algorithms For Stochastic Flow Shop No Wait Scheduling, Harpal Maini, Ubirajara R. Ferreira
Electrical Engineering and Computer Science - Technical Reports
ln this paper we present Genetic Algorithms - evolutionary algorithms based on an analogy with natural selection and survival of the fittest - applied to an NP Complete combinatorial optimization problem: minimizing the makespan of a Stochastic Flow Shop No Wait (FSNW) schedule. This is an important optimization criteria in real-world situations and the problem itself is of practical significance. We restrict our applications to the three machine flow shop no wait problem which is known to be NP complete. The stochastic hypothesis is that the processing times of jobs are described by normally distributed random variables. We discuss how …
Binary Resolution In Surface Reasoning, William C. Purdy
Binary Resolution In Surface Reasoning, William C. Purdy
Electrical Engineering and Computer Science - Technical Reports
Intuition suggests the hypothesis that everyday human reasoning is conducted in the written or spoken natural language, rather than in some disparate representation into which the surface language is translated. An examination of human reasoning reveals patterns of inference that parallel binary resolution. But any standard implementation of resolution requires Skolemization. Skolemization would seem an unlikely component of human reasoning. This appears to contradict the hypothesis that human reasoning takes place at the surface. To reconcile these observations, this paper develops a new rule of inference, which operates on surface expressions directly. This rule is shown to produce results which …