Neuro-Symbolic Integration is an interdisciplinary area that endeavours to unify neural networks and symbolic logic. The goal is to create a system that combines the advantages of neural networks (adaptive behaviour, robustness, tolerance of noise and probability) and symbolic logic (validity of computations, generality, higherorder reasoning). Several different approaches have been proposed in...

We propose SHERLOCK a novel problemsolving application based on neuro-symbolic networks. The application takes a knowledge base and rules in the form of a logic program, and compiles it into a connectionist neural network that performs computations. The network’s output signal is then translated back into logical form. SHERLOCK allows to compile logic programs either to classical neuro-symbolic...

The paper presents a methodology for designing the structure of a fuzzy neural network in a multi-modular connectionist system for classification purposes and illustrates the methodology on the task of phoneme recognition of the 43 phonemes in New Zealand English. The results show that by using this methodology the recognition rate can be improved significantly when compared to the recognition ...

The problem of disturbance rejection in the control of nonlinear systems with additive disturbance generated by some unforced nonlinear systems, was formulated and solved by {itshape Mukhopadhyay} and {itshape Narendra}, they applied the idea of increasing the order of the system, using neural networks the model of multilayer perceptron on several systems of varying complexity, so the objective...

The goal of this work is to compare fuzzy, neural network and neuro-fuzzy approaches to the control of mobile robots. The rst part of this paper is devoted to the formal framework of fuzzy controllers. Results of an example of their use for a mobile robot are discussed. As an experimental platform, the Khepera mobile robot is used. The same example is studied using artiicial neural networks. Fo...

Abstract – This paper investigates the identification of nonlinear systems by neural networks. As the identification methods, Feedforward Neural Networks (FNN), Radial Basis Function Neural Networks (RBFNN), Runge-Kutta Neural Networks (RKNN) and Adaptive Neuro Fuzzy Inference Systems (ANFIS) based identification mechanisms are studied and their performances are comparatively evaluated on a thr...

Condition monitoring and diagnosis systems capable of identifying machining system defects and tiieir location are essential for unmanned machining Unattended (or minimally manned) machining would result in increased capital equipment utilization, thus substantially reducing the manufacturing costs. A review of tool monitoring systems and techniques and their components and the Multiple Princip...

in common classical methods of cavity depth estimation through microgravity data, usually when a pre-geometrical model is considered for the cavity shape, the simple geometrical models of sphere, vertical cylinder and horizontal cylinder are commonly used. it is obviously an important fact that in real conditions the shapes of the cavities are not exactly sphere, horizontal cylinder or vertical...

This research aims to predict PV output power by using different neuro-evolutionary methods. The proposed approach was evaluated by a data set, which was collected at 5-minute intervals in the photovoltaic laboratory of Niroo Research Institute of Iran (Tehran). The data has been divided into three intervals based on the amount of solar irradiation, and different neural networks were used for p...