Rule weights often have been used to improve the classification accuracy without changing the position of antecedent fuzzy sets. Recently, fuzzy versions of confidence and support merits from the field of data mining have been widely used for rules weighting in fuzzy rule based classifiers. This paper proposes an evolutionary approach for learning rule weights and uses more flexible equations, ...

Business rule management is the task of storing and maintaining company-specific decision rules and business logic that is queried frequently by application users. These rules can impede efficient query processing when they require the business rule engine to resolve semantic hierarchies. To address this problem, this work discusses hierarchical indexes that are performance and storageconscious...

In this paper, we describe an implementation of analog neural network system with on-line learning capability. A learning rule using the simultaneous perturbation is adopted. Compared with usage of the ordinary back-propagation method, we can easily implement the simultaneous perturbation learning rule. The neural system can monitor weight values and an error value. The exclusive OR problem and...

Classical experiments on spike timing-dependent plasticity (STDP) use a protocol based on pairs of presynaptic and postsynaptic spikes repeated at a given frequency to induce synaptic potentiation or depression. Therefore, standard STDP models have expressed the weight change as a function of pairs of presynaptic and postsynaptic spike. Unfortunately, those paired-based STDP models cannot accou...

Neural network models offer a theoretical testbed for the study of learning at the cellular level. The only experimentally verified learning rule, Hebb’s rule, is extremely limited in its ability to train networks to perform complex tasks. An identified cellular mechanism responsible for Hebbian-type long-term potentiation, the NMDA receptor, is highly versatile. Its function and efficacy are m...

It is shown that the Kronrod extension to the «-point Gauss integration rule, with respect to the weight function (1 x2)V~"2, 0 < M < 2, ju i= 1, is of exact precision 3n + 1 for n even and 3n + 2 for n odd. Similarly, for the (n-t-l)-point Lobatto rule, with — V¡ < M < 1, u ^ 0, the exact precision is 3n for n odd and 3n + 1 for n even.

We propose a modular neural-network structure for implementing the Bayesian framework for learning and inference. Our design has three main components, two for computing the priors and likelihoods based on observations and one for applying Bayes’ rule. Through comprehensive simulations we show that our proposed model succeeds in implementing Bayesian learning and inference. We also provide a no...

The combination of evolutionary algorithms and ANN has been a recent interest in the field of research. Hopfield model is a type of recurrent neural network which has been widely studied for the purpose of associative memories. In the present work, this Hopfield Model of feedback neural networks has been studied with Monte Carlo adaptation learning rule and one evolutionary searching algorithm ...

In a general social choice framework where the requirement of strategyproofness may not be sensible, we call a social choice rule fully sincere if it never gives any individual an incentive to vote for a less-preferred alternative over a more-preferred one and provides an incentive to vote for an alternative if and only if it is preferred to the default option that would result from abstaining....

In a landmark paper from 1986, Kawaguchi and Kyan show that scheduling jobs according to ratios weight over processing time –also known as Smith’s rule– has a tight performance guarantee of (1 + √ 2)/2 ≈ 1.207 for minimizing the weighted sum of completion times in parallel machine scheduling. We prove the counterintuitive result that the performance guarantee of Smith’s rule is not better than ...