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Search Results: 1 - 10 of 14231 matches for " Neural Networks "
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Prediction of Electrical Output Power of Combined Cycle Power Plant Using Regression ANN Model  [PDF]
Elkhawad Ali Elfaki, Ahmed Hassan Ahmed
Journal of Power and Energy Engineering (JPEE) , 2018, DOI: 10.4236/jpee.2018.612002
Abstract: Recently, regression artificial neural networks are used to model various systems that have high dimensionality with nonlinear relations. The system under study must have enough dataset available to train the neural network. The aim of this work is to apply and experiment various options effects on feed-foreword artificial neural network (ANN) which used to obtain regression model that predicts electrical output power (EP) of combined cycle power plant based on 4 inputs. Dataset is obtained from an open online source. The work shows and explains the stochastic behavior of the regression neural, experiments the effect of number of neurons of the hidden layers. It shows also higher performance for larger training dataset size; at the other hand, it shows different effect of larger number of variables as input. In addition, two different training functions are applied and compared. Lastly, simple statistical study on the error between real values and estimated values using ANN is conducted, which shows the reliability of the model. This paper provides a quick reference to the effects of main parameters of regression neural networks.
Road and Tunnel Extraction from SPOT Satellite Images Using Neural Networks  [PDF]
Nima Ghasemloo, Mohammad Reza Mobasheri, Ahmad Madanchi Zare, Mehran Memar Eftekhari
Journal of Geographic Information System (JGIS) , 2013, DOI: 10.4236/jgis.2013.51007

Road extraction from the satellite images can be used in producing road maps as well as managing and developing geospatial databases. In this work, the extraction of roads from SPOT satellite images using artificial neural network has been studied. Then the location of tunnel is extracted from image using digital elevation information. Also it is tried to enhance the precision of the road extraction method using spectral information as well as texture and morphology. The method was implemented on SPOT satellite images of Tabrizand Miyaneh (Iran). The results of this research indicate that it would be possible to promote the precision of road extraction by using texture and morphology in image classifycation using neural networks. Finally the location of tunnel was extracted by digital elevation information. Junctions of roads and mountains have high potential in locating the tunnel. For this reason, in this study, the junctions of roads and mountains were also detected and used.

Least Action Trajectory in Neural Networks  [PDF]
Ellison C. Castro, Bhazel Anne R. Pelicano
Open Journal of Applied Sciences (OJAppS) , 2013, DOI: 10.4236/ojapps.2013.33B002

The study of complex networks had developed over the years to include systems such as traffic, predator-prey interactions, financial market, and even the world wide web. Complex network studies encompass biology, chemistry, physics, and even engineering and economics [1-6]. However, the dynamics of such complex networks are yet to be understood fully [7,8]. In this paper, we will be focusing mostly on the possible learning ability in a complex network. To do this, an optimization process is used via Wiener process [9,10]. It is apparent from the sample lattice shown that the final position was not a basis of the transition probability, or it was never used to calculate the probability, since the transition probability only considers the current position. The final point is reached because of the orientation of the edges, where each edge is facing the final point, an aspect of the nervous system (afferent and efferent nerves) [11-13]. No matter how random the orientation of the neurons is, each directs to the central nervous system for processing and is transmitted away for reaction.

Complex Valued Recurrent Neural Network: From Architecture to Training  [PDF]
Alexey Minin, Alois Knoll, Hans-Georg Zimmermann
Journal of Signal and Information Processing (JSIP) , 2012, DOI: 10.4236/jsip.2012.32026
Abstract: Recurrent Neural Networks were invented a long time ago, and dozens of different architectures have been published. In this paper we generalize recurrent architectures to a state space model, and we also generalize the numbers the network can process to the complex domain. We show how to train the recurrent network in the complex valued case, and we present the theorems and procedures to make the training stable. We also show that the complex valued recurrent neural network is a generalization of the real valued counterpart and that it has specific advantages over the latter. We conclude the paper with a discussion of possible applications and scenarios for using these networks.
The Use of Neural Network and Portfolio Analysis in Forecasting Share Prices at the Stock Exchange
Przemyslaw Stochel
Computer Science , 2000,
Abstract: The article presents the use of neural networks in decision making process on the capital market. The author tried to show the efficiency of established solution in Polish reality which features different conditions in comparison with the markets of higher developed countries. The aim of the paper was to prove that neural networks are flexible tools which on one hand might be adjusted to investor's requirements and on the other, can reduce equirements to his experience. The article is based on the author's own research carried out by modelling neural network operation with a simulation program. The established solutions are input which employs stocks portfolio computed on the basis of Markowitz portfolio theory and Sharpe's model. According to the established propositions, the portfolio created in such a way is modified by neutral network in order to optimise a criterion which maximises the income of such a modified portfolio. A detailed genesis of the established input vector and network structure are presented. It allows the reader to carry out his own research and create his own attitude towards applied values. The research results based on a real stock market database with the use of one-output networks predicting thc price of a single company - Agros as well as networks predicting the desirable structure of the whole portfolio are presented. The effect of the network structure leaming parameters, input vector (not only as to the input quantity but also as to period of time they were collected) was examined. The dependence between the factors mentioned above such as input vector and network structure were discussed. lt seems that the presented paper has proved that some not widely spread methods with neural networks can become at competitive tool to optimisation methods.
Fast Automatic Configuration of Artificial Neural Networks Used for Binary Patterns Recognition
Adrian Horzyk
Computer Science , 2001,
Abstract: This paper presents a powerful method of an automatically generated architecture of neural networks used for binary pattems recognition, which can quickly and automatically reduce synapses in a way of minimally reducing a quality of recognition and a quality of generalization. Moreover, this method computes all weights in two runs over a leaming sequence, what makes this method very fast. First, the method calculates all binary features for each pattem and then weights are computed. Furthermore, there is a quality of generalization considered because it is one of the most important factors of recognition while using neural networks.
Human dental pulp stem cells differentiate into neural precursors but not into mature functional neurons  [PDF]
Riikka Aanismaa, Jenna Hautala, Annukka Vuorinen, Susanna Miettinen, Susanna Narkilahti
Stem Cell Discovery (SCD) , 2012, DOI: 10.4236/scd.2012.23013
Abstract: Large numbers of neuronal cells are needed for regenerative medicine to treat patients suffering from central nervous system diseases and deficits such as Parkinson’s disease and spinal cord injury. One suggestion has been the utilization of human dental pulp stem cells (hDPSCs) for production of neuronal cells which would offer a patient-specific cell source for these treatments. Neuronal differentiation of hDPSCs has been described previously. Here, we tested the differentiation of DPSCs into neuronal cells with previously reported protocol and characterized the cells according to their morphology, gene and protein expressions and most importantly according to their spontaneous electrical functionality with microelectrode array platform (MEA). Our results showed that even though hDPSC-derived neural progenitor stage cells could be produced, these cells did not mature further into functional neuronal cells. Thus, utilization of DPSCs as a cell source for producing grafts to treat neurological deficits requires more efforts before being optimal.
Molecular network using molecular circuit for drug delivery use  [PDF]
Narongchai Moongfangklang, Somsak Mitatha, Surasak Pipatsart, Preecha P. Yupapin
Journal of Biomedical Science and Engineering (JBiSE) , 2012, DOI: 10.4236/jbise.2012.57046
Abstract: A novel design of molecular networks for drug delivery application using a PANDA ring resonator is proposed. By using the intense optical vortices generated within the PANDA ring resonator, the required molecules can be trapped and moved (transported) dynamically within the wavelength router and bus networks, in which the required drug delivery can perform within the wavelength router before reaching the required destination. PANDA ring is a modified optical add/drop filter. It is a name of Chinese bear, which is used to name the device by the authors. The advantage of the proposed system is that the drug delivery networks can perform within the tiny system (thin film device), where the large molecular drug networks such as ring, star and bus networks are also proposed, in which the applications such as Alzheimers’ and Parkinson diagnosis, blood circulation networks and in situ surgery operation are discussed.
Artificial Intelligence for Speech Recognition Based on Neural Networks  [PDF]
Takialddin Al Smadi, Huthaifa A. Al Issa, Esam Trad, Khalid A. Al Smadi
Journal of Signal and Information Processing (JSIP) , 2015, DOI: 10.4236/jsip.2015.62006
Abstract: Speech recognition or speech to text includes capturing and digitizing the sound waves, transformation of basic linguistic units or phonemes, constructing words from phonemes and contextually analyzing the words to ensure the correct spelling of words that sounds the same. Approach: Studying the possibility of designing a software system using one of the techniques of artificial intelligence applications neuron networks where this system is able to distinguish the sound signals and neural networks of irregular users. Fixed weights are trained on those forms first and then the system gives the output match for each of these formats and high speed. The proposed neural network study is based on solutions of speech recognition tasks, detecting signals using angular modulation and detection of modulated techniques.
MetalloPred: A tool for hierarchical prediction of metal ion binding proteins using cluster of neural networks and sequence derived features  [PDF]
Pradeep Kumar Naik, Piyush Ranjan, Pooja Kesari, Sankalp Jain
Journal of Biophysical Chemistry (JBPC) , 2011, DOI: 10.4236/jbpc.2011.22014
Abstract: Given a protein sequence, how can we identify whether it is a metalloprotein or not? If it is, which main functional class and subclasses it belongs to? This is an important biological question because they are closely related to the biological function of an uncharacterized protein. Particularly, with the avalanche of protein sequences generated in the post genomic era and since conventional techniques are time consuming and expensive, it is highly desirable to develop an automated method by which one can get a fast and accurate answer to these questions. Here, a top-down predictor, called MetalloPred, is developed which consists of 3 level of hierarchical classification using cascade of neural networks from sequence derived features. The 1st layer of the prediction engine is for identifying a query protein as metalloprotein or not; the 2nd layer for the main functional class; and the 3rd layer for the sub-functional class. The overall success rates for all the three layers are higher than 60% that were obtained through rigorous cross-validation tests on the very stringent benchmark datasets in which none of the proteins has 30% sequence identity with any other in the same class or subclass. MetalloPred achieved good prediction accuracies and could nicely complement experimental approaches for identification of metal binding proteins. MetalloPred is freely available to be used in-house as a standalone and is accessible at http://www.juit.ac.in/assets/Metallopred/.
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