In this paper, a new neural network enhanced synchronized control approach is proposed for multiple robotic manipulators systems (MRMS) based on leader-follower network communication topology. The justification of introducing two adaptive Radial Basis Function Neural Networks (RBF NN), also called neuro agents, is to facilitate the whole control system design and analysis. Otherwise such design...

This paper first presents a discussion of the reasoning and method for combining neural networks and fuzzy logic. The problem of moving a robotic arm in the presence of an obstacle is discussed. Several neuro-fuzzy controllers are trained using sample data obtained from a human’s control of a robotic arm. Their performance is quantified and compared. It is shown that the definition of the fuzzy...

A strategy for dynamic system failure detection and diagnosis is proposed in this paper, based on sliding mode observers, employed for residual generation with discrimination among the error subspaces, and a fuzzy neural network, used for pattern classification. A control reconfiguration scheme is proposed, employing both the fault dianosis information and the robust observer generated data. Th...

This paper presents a stop-loss maximum return (SLMR) trading strategy based on improving the classic moving average technical indicator with neural networks. We propose an improvement in the efficiency of the long term moving average by using the limited recursion in Elman Neural Networks, jointly with hybrid neuro-symbolic neural network, while still fully keeping all the learning capabilitie...

This paper proposes an ensemble artificial neuro-molecular system for motion recognition for a wearable sensor system with 3-axis accelerometers. Human motions can be distinguished through classification algorithms for the wearable sensor system of two 3-axis accelerometers attached to both forearms. Raw data from the accelerometers are pre-processed and forwarded to the classification algorith...

This paper presents an adaptive neuro-fuzzy inference system (ANFIS) for USD/JPY exchange rates forecasting. Previous work often used time series techniques and neural networks (NN). ANFIS can be used to better explain solutions to users than completely black-box models, such as NN. The proposed neurofuzzy rule based system applies some technical and fundamental indexes as input variables. In o...

The search for variants of effective neural behavior is a major requirement for the identification of novel neuro-dynamical control principles. Evolutionary algorithms are successfully used to search for such controllers. But neuro-evolution tends to find similar, well performing solutions when run multiple times, instead of many, perhaps also weaker performing, but neuro-dynamically highly int...

Different techniques are available for the prediction of stock market. Very popular some of these are Neural Network, Data Mining, Hidden Markov Model(HMM) And Neuro-Fuzzy system. From these Neural Network and Neuro-Fuzzy Systems are the most leading machine learning techniques in stock market index prediction area. Other traditional methods do not cover all possible relation of stock price mov...

Classification of texture patterns is one of the most important problems in pattern recognition. In this paper, we present a classification method based on the Discrete Cosine Transform (DCT) coefficients of texture images. As DCT works on gray level images, the color scheme of each image is transformed into gray levels. For classifying the images using DCT, we used two popular soft computing t...

Classification of texture pattern is one of the most important problems in pattern recognition. In this paper, we present a classification method based on the Discrete Cosine Transform (DCT) coefficients of texture image. As DCT works on gray level image, the color scheme of each image is transformed into gray levels. For classifying the images using DCT we used two popular soft computing techn...