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Effective generation of hash function is very important for an achievement of a security of today networks. A cryptographic hash function is a transformation that takes an input and returns a fixed-size value, which is called the hash value. An artificial neural network (ANN), as a possible approach, could be used for the hash function generation. The performance of the ANN was validated by software...
This paper discusses the development and application of a decomposition neural network rule extraction algorithm for nonlinear regression problems, the algorithm is called the piece-wise linear artificial neural network or PWL-ANN algorithm. Rules in the form of linear equations are generated by approximating the sigmoid activation functions of the hidden neurons in an artificial neural network (ANN)...
This paper presents the design and implementation of two spiking neural network based sorting algorithms with time complexity of ????????. These algorithms are inspired from artificial neural network based sorting algorithms. A number comparator circuit using spiking network as a building block has been proposed and been used in the sorting algorithms to compare numbers. This comparator circuit is...
The choice of weight initialization routines is one of the important choices to be made for improving the training efficiency of an artificial neural network. In this paper, we analyze the affect of many known weight initialization routines, on training of an artificial neural network, when it was trained with a second order scaled conjugate gradient training algorithm. A number of experiments were...
Artificial neural networks (NN) have shown a significant promise in difficult tasks like image classification or speech recognition. Even well-optimized hardware implementations of digital NNs show significant power consumption. It is mainly due to non-uniform pipeline structures and inherent redundancy of numerous arithmetic operations that have to be performed to produce each single output vector...
The purpose of this research is to improve performance of the Hybrid Scene Analysis - Neural Network indoor localization algorithm applied in Real-time Locating System, RTLS. A properly customized structure of Neural Network and training algorithms for specific operating environment will enhance the system's performance in terms of localization accuracy and precision. Due to nonlinearity and model...
Artificial neural networks are one of the most popular and promising areas of artificial intelligence research. Training data containing outliers are often a problem for supervised neural networks learning algorithms that may not always come up with acceptable performance. Many robust learning algorithms have been proposed so far to improve the performance of neural networks in the presence of outliers...
For today the unit neural networks are widely used to solve various problems. In this regard the issue of developing learning algorithm that would be able to optimize the structure of neural networks dynamically is very important. The existence of such a method would allow the researcher to get the structure of the neural network that would be best-answered domain and available input data quickly.
Training time is an important consideration in classification practices. Various algorithms like SVM usually suffer from high computational cost during the training process. In this paper, we proposed a hybrid classification algorithm, by combining RNN and SVM together. RNN is a rapid neural network with randomly generated input weights, which performs as a fast, light weighted data selector. Selected...
In this paper, a Neural Network Deployment (NND) algorithm is presented to realize and synthesize Multi-Valued Logic (MVL) functions. The algorithm is combined with back-propagation learning capability and MVL operators. The operators are used to synthesize the functions. Consequently the synthesized expressions are applied by the MVL neural operators. The advantages of NND-MVL algorithm are demonstrated...
Various models have been suggested to evaluate and study the problem solving and decision making techniques of students. In this work, the Artificial Neural Network is used to evaluate the performance of students of university. Artificial Neural Networks are massively interconnected networks in parallel of simple elements (usually adaptable), with hierarchic organization, which try to interact with...
Artificial neural networks (NNs) are traditionally designed with distinctly defined layers (input layer, hidden layers, output layer) and accordingly network design techniques and training algorithms are based on this concept of strictly defined layers. In this paper, a new approach to designing neural networks is presented. The structure of the proposed NN is not strictly defined (each neuron may...
For the disadvantages of BP neural network(NN), which easily traps into a local optimum and is sensitive to the initial parameters of the network, an algorithm for the optimization of the architecture, the weights and the thresholds of neural networks using an improved gene expression programming(IGEP) was presented. First, the basic principles of BP neural network (BP-NN) and GEP was introduced and...
This study aimed to propose, a different architecture of a collision detection neural network (DCNN). The ability to detect and avoid collision is very important for mobile intelligent machines. However many artificial vision systems are not yet able to quickly and cheaply extract the wealth information. This network, which has been particularly reviewed, has enabled us to solve with a new approach...
The design of Artificial Neural Network (ANN) is a typical task as it is depends on human experience. There are few techniques like the Back-Propagation algorithm and nature inspired meta-heuristic are one of the most widely used and popular technique for optimizing feed forward neural network training. Artificial Bee Colony (ABC) algorithm is nature inspired meta-heuristic approach based on behavior...
In this paper, a High Precise Optimization Algorithm for manipulating multi-layered feed-forward neural network is studied. Its basic principle is: defining neural network average error as objective function, weights and thresholds as design variables, through design variables rationally sorted, objective function is dynamically formed. Compared the new method with BP, the optimum step-length can...
Understanding the influence of some factors on a particular phenomenon can be very relevant in many cases of decision-making. An example would be the identification of the level of influence that factors such as smoking, stress and lack of exercise have on the predisposition to heart disease. Knowing which of these inputs are relevant for a person to become a cardiac patient, it is possible to take...
In this paper a loan default prediction model was constricted using two attribute detection functions, resulting in two data-sets with reduced attributes and the original data-set. A supervised two-layer feed-forward network, with sigmoid hidden neurons and output neurons is used to produce the prediction model. Back propagation learning algorithm was used for the network. Furthermore three different...
This paper mainly describes a search module, based on the BP neural network model, in Beijing Vocational College of Electronic Science Student Work Management System. The module is for selecting the appropriate work based on the machine learning. This module creates the BP model for every user and adjusts the weight and threshold of the BP model while students use the search module to browse other...
This paper identifies the suitable learning algorithm for neural network based on-line speed estimator in sensorless induction motor drives. The performance of sensorless controlled induction motor drives depends on the accuracy of the estimated speed. Conventional estimation techniques being mathematically complex require more execution time resulting in poor dynamic response. The nonlinear mapping...
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