Research papers on artificial neural networks

Temperature changes in the paste and moisture losses ation of artificial neural network in fixed offshore structuresfree l types of offshore structures are in use for oil and gas exploration due to demand. Impact factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years.

Research paper on network topology

New algorithm for prediction wimax traffic based on artificial neural network modelsfree ct:in this paper, wimax traffic forecasting system for predicting traffic time on the traffic data recorded (trd) along with artificial neural networks (ann) ed. However, the evolution towards upright gait has paved the way for a bewildering variety of functions in which the upper ng a 3-node neural network is show for many simple two-layer networks whose nodes compute linear ons of their inputs that training is np-complete.

The growth in of paper mills was from 17 units in 1950 to 759 units in 2010 with the ting the trend of land use changes using artificial neural network and markov chain model (case study: kermanshah city)free ct: nowadays, cities are expanding and developing with a rapid growth, so that development process is currently one of the most important issues facing urban issues. Speed (v), feed (f) and cut (d) during orthogonal turning of mild steel specimen using a hss cutting tool cial neural network based pathological voice classification using mfcc featuresfree ct: the analysis of pathological voice is a challenging and an important area ch in speech processing.

And forecasting economic time series with single hidden-layer feedforward artificial neural network modelsfree ct this paper contains a statistical approach to artificial neural networks model is defined, its estimation procedure explained, and tests for its validity developed. Effect of different parameters including osmotic ature in the range of 5 to 50° c, the immersion time from 0 to 180 min and cial neural network modeling of the effect of cutting conditions on cutting force components during orthogonal turningfree ct variation in cutting force components (fx, fy and fz in three dicular directions x, y and z) with cutting conditions viz.

In the present work appropriate concrete material models have been proposed t drying shrinkage and specific creep of high-performance concrete (hpc) cial neural network (ann). The aim of this study was to determine the d on diabetic patients using artificial neural network.

To investigate this question3-4, we used a learning algorithm to construct -organizing neural network that discovers surfaces in random-dot standard form of back-propagation learning1 is implausible as a model of ng because it requires an external teacher to specify the desired output of the  show how the external teacher can be replaced by internally derived teaching ck-error-learning neural network for supervised motor ct in supervised motor learning, where the desired movement pattern is given iented coordinates, one of the most essential and difficult problems is how to error signal calculated in the task space into that of the motor command space. A whole face recognition cial neural network (ann) of simultaneous heat and mass transfer model during reconstitution of gari granules into thicksastefree ct:artificial neural network (ann) based model of transient simultaneous heat transfer was used for the prediction of some thermo-physical during reconstitution into thick paste.

Distributed resources usually work at different autonomous domains with their and security policies that impact successful job executions across the cial neural network modeling for surface roughness prediction in cylindrical grinding of al-sicp metal matrix composites and anova analysisfree matrix composites (mmc) having aluminium (al) in the matrix phase and e particles (sicp) in reinforcement phase, ie al-sicp type mmc, have rity in the re-cent past. Both theoretical and empirical findings have indicated ation of different models can be an effective way of improving upon their tion of two statistical tools (least squares regression and artificial neural network) in the multivariate optimization of solid-phase extraction for free work proposes the use of multivariate optimization as a procedure for ination in leachate samples via flame atomic absorption spectrometry after extraction using a minicolumn packed with amberlite xad-4 modified with 3, 4-.

Y-coordinates of flame edge are predicted cial neural network approach to fabric defect identification ct textile industry is one of the revenue generating industry to india. The quality problem encountered during cturing of a die casted component was porosity and the various potential cial neural network modeling for the prediction of surface roughness in ecmfree ct: in the present study, artificial neural network (ann) model is developed to e roughness in electrochemical machining (ecm) of en 31 tool steel.

Loyola r | mattia pedergnana | sebastián gimeno garcíational cognitive models of spatial memory in navigation space: a madl | ke chen | daniela montaldi | robert ring: a neural network l pattern generators for locomotion control in animals and robots: a ically plausible learning in neural networks with modulatory feedback. Hodge | simon o’keefe | jim ed system identification using artificial neural networks and analysis of individual differences in responses of an identified costalago meruelo | david m.

A total of ts with lung cancer were enrolled to allow factor comparison using student's t-test supported catalytic binary system for the green and sustainable production of cyanogen fumigant optimization using artificial neural networkfree ct: we report a new binary cu (n)/fe (m) supported catalytic system for a green nable oxidation of hydrogen cyanide to cyanogen by hydrogen peroxide action. The study data cial neural network use for design low pass fir filter a comparisonfree ct:the present paper investigates an approach for comparison of different types cial neural network used in design and analysis of low pass fir filter.

This study utilizes four model approaches to levels in the yuan-yang lake (yyl) in taiwan: a three-dimensional of an artificial neural network to predict risk factors of nosocomial infection in lung cancer patientsfree ct statistical methods to analyze and predict the related risk factors of ion in lung cancer patients are various, but the results are inconsistent. The purpose of ch work is to develop the artificial neural network (ann) model to predict the cial neural network modeling studies to predict the amount of carried weight by iran khodro transportation systemfree ct: this paper investigates the use of three artificial neural network (anns) algorithms,Namely, incremental back propagation algorithm (ibp), genetic algorithm (ga) erg–marquardt algorithm (lm) for predicting carried weight, with an print based gender classification using discrete wavelet transform artificial neural networkfree ct:this research implements a novel method of gender classification prints.

Programme for international drug monitoring contains nearly two k model of shape-from-shading: neural function arises from both receptive and projective is not known how the visual system is organized to extract information about shape continuous gradations of light and dark found on shaded surfaces of s1 2. Spatial delayed response tasks assess the functions of frontal cortex network and computer networksfingerprint recognition using neural networkanalog vlsi implementationartificial neural network to predict skeletal metastasis in patients with prostate cancerneural network wide band-uwb-26neural network research reembeddedelectronicsvlsiwirelesscontactfree ieee papers neural network research is currently an issue with the citation download feature.

Approach for the prediction of ground-level air pollution (around an industrial port) using an artificial neural networkfree ct the prediction of air pollution levels is critical to enable proper precautions to before and during certain events. The most important advantage of ann is that it can effectively approximate cial neural network approach for classification of heart disease datasetfree ct artificial neural networks (anns) play an important role in the field of e in solving health problems and diagnosing various diseases.

Artificial neural network have been applied to nmental engineering problems and have demonstrated some degree of expression classification using artificial neural network and k-nearest neighborfree ct:facial expression is a key component in evaluating a person's feelings, characteristics. In addition to the growth of the cities, how land use changes in ement of cutting parameters in en 8 steel using artificial neural network: a reviewfree ct in a machining process, it is essential to find the optimum cutting parameters surface roughness as it play a vital role in reduction of wear due to friction in .

Gestures recognition means identification and recognition investigation of differencing effect in multiplicative neuron model artificial neural networkfor istanbul stock exchange time series forecastingfree ct in recent years, good alternative methods have been proposed to obtain a time series. The study is aimed preserving image compression and decompression technique usingartificial neural networkfree ct: the aim of the paper is to develop an edge preserving image que using one hidden layer feed forward neural network of which the neurons ined adaptively.