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Bousoño-Calzón, C.; Manning, M.
doi: 10.1007/BF01421958pmid: N/A
The Hopfield neural network is proposed as a method for solving the Quadratic Assignment Problem. The study involves determining the relevant parameter constraints, and provides a comparison of the performance of the Hopfield model with that of a conventional approach.
doi: 10.1007/BF01421959pmid: N/A
Backpropagation neural networks have been applied to prediction and classification problems in many real world situations. However, a drawback of this type of neural network is that it requires a full set of input data, and real world data is seldom complete. We have investigated two ways of dealing with incomplete data — network reduction using multiple neural network classifiers, and value substitution using estimated values from predictor networks — and compared their performance with an induction method. On a thyroid disease database collected in a clinical situation, we found that the network reduction method was superior. We conclude that network reduction can be a useful method for dealing with missing values in diagnostic systems based on backpropagation neural networks.
doi: 10.1007/BF01421960pmid: N/A
Constraint Satisfaction Problems (CSPs) are in general NP-hard, and a general deterministic polynomial time algorithm is not known. They play a central role in real-life problems. The satisfaction of a Conjunctive Normal Form (CNF-SAT)is the core of any CSP. We present a new modelisation technique for any CSP with finite variable domains, and, in particular, for solving CNF-SAT. The knowledge representation is based on two fundamental types of constraint: the choice constraint, and the exclusion constraint. These models are then implemented by means of several different neural networks, some based on backpropagation learning and others on different procedures. All these networks are trained through a supervised procedure, and learn to efficiently solve CNF-SAT. The results of significant tests are described: they show that some networks can effectively solve the proposed problems.
Likas, A.; Kontoravdis, D.; Stafylopatis, A.
doi: 10.1007/BF01421961pmid: N/A
A new approach is presented for finding near-optimal solutions to discrete optimisation problems that is based on the cooperation of two modules: an optimisation module and a constraint satisfaction module. The optimisation module must be able to search the problem state space through an iterative process of sampling and evaluating the generated samples. To evaluate a generated point, first a constraint satisfaction module is employed to map that point to another one satisfying the problem constraints, and then the cost of the new point is used as the evaluation of the original one. The scheme that we have adopted for testing the effectiveness of the method uses a reinforcement learning algorithm in the optimisation module and a general deterministic constraint satisfaction algorithm in the constraint satisfaction module. Experiments using this scheme for the solution of two optimisation problems indicate that the proposed approach is very effective in providing feasible solutions of acceptable quality.
Zhao, Jun; Kearney, Garrett; Soper, Alan
doi: 10.1007/BF01421962pmid: N/A
The classification of facial expressions by cascade-correlation neural networks [1] is described. A success rate of 100% over the training data for each of six categories of emotion —happiness, sadness, anger, surprise, fear and disgust — and of up to 87.5% over the same categories for the test data, has been achieved. By using single emotion nets for each category, together with a Net for Resolution, the results represent a 12.5% success rate beyond what was achieved by a single net classifying over all six emotion categories. Face data in the form of 10 hand measurements made on 94 well validated full face photographs [2] provided the input data after normalisation. These measures, among others, had previously been shown to discriminate between emotions [3].
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