In the paper, a new optimal learning algorithm for a neo-fuzzy neuron (NFN) is proposed. The algorithm is characteristic in that it provides online tuning of not only the synaptic weights, but also the membership functions parameters. The proposed algorithm has both the tracking and filtering properties, so the NFN can be effectively used for prediction, filtering, and restoration of non-statio...

Machine learning based forecasting are found better to manual and statistical methods in estimating compressive strength of concrete structures. However, there is need exploring an effective, automated accurate predictor for this domain. This article proposes artificial electric field algorithm-based neuro-fuzzy network (AEFA+NFN) prediction A single hidden layer neural (SHNN) used as the base ...

large-scale climatic signals including ocean-atmosphere interactions, are the main factors influencing the earth’s climatic oscillations and are the most important indices in predicting of climate variables. in this research, precipitation in the next month was predicted by applying artificial neural network (ann), neuro-fuzzy network (nfn), and multiple linear regression (mlr) in semnan synopt...

In this paper, an adaptive control method for trajectory tracking of robot manipulators, based on new neuro-fuzzy modelling is presented. The proposed control scheme uses a three-layer neural fuzzy network (NFN) to estimate system uncertainties. The function of robot system dynamics is first modelled by a fuzzy system, which in the sequel is approximated by a combination of high order neural ne...

Normal Form Nested (NFN ) programs have recently been introduced in order to allow for enriching the syntax of disjunctive logic programs under the answer sets semantics. In particular, heads of rules can be disjunctions of conjunctions, while bodies can be conjunctions of disjunctions. Different to many other proposals of this kind, NFN programs may contain variables, and a notion of safety ha...

The hypergeometric functions nFn−1 are higher transcendental functions, but for certain parameter values they become algebraic. This occurs, e.g., if the defining hypergeometric differential equation has irreducible but imprimitive monodromy. It is shown that many algebraic nFn−1’s of this type can be represented as combinations of certain explicitly algebraic functions of a single variable, i....

This paper presents a new algorithm for speed observation of three-phase induction machines, based on a Neo-Fuzzy-Neuron (NFN) algorithm with real time training, which does not require previous training. The main characteristics of this novel observer are: the quick convergence, the good performance at wide speed range, and the robustness to load and some parametric variations. This observer re...

Normal Form Nested (NFN) programs have recently been introduced in order to allow for enriching the syntax of disjunctive logic programs under the answer sets semantics. In particular, heads of rules can be disjunctions of conjunctions, while bodies can be conjunctions of disjunctions. Different to many other proposals of this kind, NFN programs may contain variables, and a notion of safety has...

By applying the recurrent functional neural fuzzy network (RFNFN) and a novel evolutionary learning algorithm this study presents an evolutionary neural fuzzy network (NFN). The proposed new evolutionary learning algorithm is based on an effective combination of the modified differential evolution (MDE) and cultural algorithm, which is called the cultural-based MDE (CMDE) method. After the four...

This is a Fuchsian equation of order n with singularities at 0, 1,∞. The local exponents read, 1− β1, . . . , 1− βn at z = 0 α1, . . . , αn at z =∞ 0, 1, . . . , n− 2, −1 + ∑n 1 (βi − αi) at z = 1 When the βi are distinct modulo 1 a basis of solutions at z = 0 is given by the functions z1−βi nFn−1 ( α1 − βi + 1, . . . , αn − βi + 1 β1 − βi + 1, ..∨.., βn − βi + 1 ∣∣∣∣ z) (i = 1, . . . , n). Her...