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This paper presents an Artificial Neural Network (ANN) design for a chaotic generator, and the training performances for a three layer ANN architecture with different number of hidden neurons. Chaotic systems can be synchronized and used for secure communication. Chaotic systems such as Lorenz attractor, Rossler attractor and Chen's system are generally implemented directly based on their definitions...
Deep Belief Network (DBN) is made up of stacked Restricted Boltzmann Machine layers associated with global weight fine-tuning for pattern recognition. However, DBN suffers from vanishing gradient problem due to the saturation characteristic of activation function. Therefore, the selection of activation function in DBN is critical to reduce the network complexity and improve performance of pattern...
We present a novel technique for texture synthesis and style transfer based on convolutional neural networks (CNNs). Our method learns feed-forward image generators that correspond to specification of styles and textures in terms of high-level describable attributes such as 'striped', 'dotted', or 'veined'. Two key conceptual advantages over template-based approaches are that attributes can be analyzed...
The conventional approach of generating clinical opinions from general blood test (GBT) results uses the deep neural network (DNN) comprised of fully-connected layers. The large number of input neurons and output neurons result in the complex DNN structure, which causes overfitting problem. However, the dimension of the input vector and the output vector cannot be reduced arbitrarily, as all GBT results...
The aim of this work is to develop a robust model for short-term prediction of Photovoltaics (PV) generation. The model is structured with algorithms that belong to the technical field of computational intelligence. This approach provides the potential to form a forecasting system with high flexibility, efficiency and customization. The paper examines various combinations of inputs, in order to fully...
This work describes the calculation parameters of the drying agent, which includes the speed of air circulation, its temperature and humidity. The result of the calculation is the establishment of radial-basis artificial neural network, which allows to determine any parameter of drying agent at any point, represented by the coordinates X, Y, Z, and located within the drying chambers. Also in this...
Internet users have to face to tremendous information from website. Clustering is a good solution to organize information. However, most clustering algorithms operate in the static situation. That means, it doesn't allow any incremental data. Certainly, this restrict is not fit to network environment, since data from internet is continuous increasing. Thus, an incremental clustering algorithm based...
System identification is the process of developing a mathematical model of a system using input and output knowledge of system. Identification of nonlinear system is well known problem due to its unpredictability and complexity. The nonlinear system for identification is Inverted Pendulum in this work which is well known benchmark system in control system theory due to it's highly nonlinear and unstable...
Due to the rapid growth in e-business and electronic payment systems, Fraud is rising in banking transactions associated with credit cards. This paper intends to develop a credit card fraud detection (CCFD) model based on Artificial Neural Networks (ANN) and Meta Cost procedure to reduce risk reputation and risk of loss. ANN strategy have been used for credit card fraud prevention and detection. Because...
In this paper, a novel model, named double-reservoir echo state networks (DR-ESN), is proposed. DR-ESN is constructed by two reservoirs which are connected in series, thus the performance of abstracting the characteristics from the prediction task is improved. A sufficient condition is provided to ensure the stability of DR-ESN. The batch gradient method and ridge regression method are utilized to...
Research in echocardiogram imaging it's very important because it allows assessing both anatomy and cardiac function, help diagnose various diseases. In this paper to find the optimal architecture of a MNN, where means finding the number of layers and nodes. In this case the Type-2 fuzzy logic gravitational search algorithm is used for optimizing the MNN for pattern recognition in echocardiogram imaging.
Dendrite morphological neurons are a type of artificial neural network that work with min and max operators instead of algebraic products. These morphological operators allow each dendrite to build a hyper-box in classification N-dimensional space. In contrast with classical perceptrons, these simple geometrical representations, hyper-boxes, allow the proposal of training methods based on heuristics...
Kernel methods and neural networks (NN) are two of the most powerful tools of machine learning to solve the engineering and science problems. In this paper, we propose kernel ridge regression (KRR) and NN to estimate the compressive strength (CS) of concrete with recycled aggregate based on the values of cement, natural aggregate, recycled aggregate, sand, and water. We collected a dataset of 182...
Data stream mining has been receiving increased attention due to its presence in a wide range of applications, such as sensor networks, banking, and telecommunication. One of the most important challenges during learning from data streams is reacting to concept drift, i.e. unforeseen changes of the stream's underlying data distribution. Traditional methods always used online learning to handle the...
Remaining useful life estimation (RUL), as an essential part in prognostics and health management (PHM), has becoming the hot issue and one of the challenging problem with the high requirement on the reliability and safety of the equipment. Extreme learning machine (ELM) is a Single-hidden Layer Feed-forward Neural Networks (SLFNs) learning algorithm which is easy to use. As the new generation of...
Unorganized neural networks — or unorganized machines - are recent developed architectures in the field of computational intelligence, in which the supervised tuning of the free parameters is restricted to the weights of the output layer, by means of a linear least square solution. The remaining weights are randomly generated and stand untrained which become the adjustment process simple and fast...
Off-line pattern recognition in speech signals is a complex task. Yet, this task becomes harder when the recognition result is required online. The present work proposes an online identification of the Portuguese language phonemes using an nonlinear autoregressive model with exogenous inputs, commonly called NARX. The process first extracts the frequency characteristics of the input speech signals...
This paper deals with the application of state space neural model to Iterative Learning Control (ILC) design. Described work addresses the issue when analytical model of the nonlinear system is not available or is hard to identify. The state space neural network can be easily trained with the use of historical data. Further it is linearised to obtain linear model possible to use with ILC technique...
The present work describes the thermal efficiency optimization of parabolic trough collectors by combining a model of artificial neural network and computational optimization techniques. A feedforward neural network architecture is trained using experimental database from parabolic trough collector operations. Rim angle, inlet and outlet fluid temperatures, ambient temperature, water flow, direct...
The emissions of on-road vehicles are studied based on a remote sensing system and artificial neural network models. A transportable vehicle emission remote sensing system is used to collect the emission data from May to August 2012 in Hefei, China. Based on these light-duty gasoline vehicle data containing the emission pollutants such as carbon monoxide, hydrocarbons, nitric oxide, and so on, artificial...
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