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This paper introduces a novel design of an artificial neural network tailored for wafer-scale integration. The presented VLSI implementation includes continuous-time analog neurons with up to 16 k inputs. A novel interconnection and routing scheme allows the mapping of a multitude of network models derived from biology on the VLSI neural network while maintaining a high resource usage. A single 20...
The research performed focus on the development of methods of building-up of the intelligent neural network modeling solutions database as well as methods of approximation aiming at empirical knowledge conservation and representation to find the best structure of the artificial neural network (ANN). The learning sample is made up of solutions of approximation of one-dimensional functions defined in...
A new method for blind separation and bearing estimation of wavefronts in a smart antenna scheme, which is based on the usage of artificial neural networks (ANN) is presented here. Because of ldquothe curse of dimensionality,rdquo especially in the cases having many antenna elements, in uniform linear, circular or planar arrays, it is important to find a method which makes it feasible to use the ANNs...
One of the main obstacles to obtain an artificial neural network with reasonable performance is the parameter setting. This work proposes a methodology to the automatic definition of RBF (radial basis function) networks with an appropriate configuration for the selected classification problems. We propose the use of a memetic algorithm in order to perform the search for networks with minimum architecture...
Quantum computation algorithms indicate possibility that non-deterministic polynomial time (NP-time) problems can be solved much faster than by classical methods. Farhi et al., have proposed an adiabatic quantum computation (AQC) for solving the three-satisfiability problem (3-SAT). We have proposed a neuromorphic quantum computation algorithm based on AQC, in which an analogy to an artificial neural...
Self organizing map (SOM) is a kind of artificial neural network with a competitive and unsupervised learning. This technique is commonly used to dataset clustering tasks and can be useful in patterns recognition problems. This paper presents an artificial neural network application to signals language recognition problem, where the image representation is given by bit signatures. The recognition...
In this paper we described a sound-source localization (SSL) system which can be applied to mobile robot and automatic control systems. A novel approach of using artificial neural network was proposed to obtain the horizontal direction angle (azimuth) of the sound source. According to humanoid characteristic only two microphones, which were attached symmetrically on both sides of the robot as its...
Autonomous robots must be able to learn and maintain models of their environments. In this context, the present work considers techniques for the classification and extraction of features from images in joined with artificial neural networks in order to use them in the system of mapping and localization of the mobile robot of Laboratory of Automation and Evolutive Computer (LACE). To do this, the...
A challenging task is to classify Internet customers based on their heterogeneous search histories of shopping in the Internet. The problem is the data pattern itself. Each transition of a customer from one page to the next in purchasing a commodity is considered as an attribute and this is a pair of data. The purchase patterns consist of usually different length for different customers. We cannot...
This research employs an artificial neural network with a variable mathematic structure that is capable of simulating a nonlinear structural system. A back-propagation neural network (BPN) is adopted to estimate outflow for an ungauged area by considering temporal distribution of rainfall-runoff and the spatial distribution of watershed environment. The nonlinear relationship among the physiographic...
A hybrid method combining artificial neural network (ANN) with genetic algorithm (GA) is discussed in this paper. A new strategy of optimization on ANN structure and weights based on multi-population GA is proposed, and the quantitative optimization of ANN is realized. The Levenberg-Marquardt(LM) algorithm is used for further training the neural network, which can avoid the weak local searching ability...
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