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Start Over Subject artificial neural network

1. A learning algorithm for a novel neural network architecture motivated by integrate-and-fire neuron model

Creator:
Mishra, Deepak, Yadav, Abhishek, and Kalra, Prem K.
Format:
bez média and svazek
Type:
model:article and TEXT
Subject:
Integrate-And-Fire Neuron Model, function approximation, classification, artificial neural network, and steepest descent method
Language:
English
Description:
In this paper, a learning algorithm for a novel neural network architecture motivated by Integrate-and-Fire Neuron Model (IFN) is proposed and tested for various applications where a multilayer perceptron (MLP) neural network is conventionally used. It is observed that inclusion of a few more biological phenomenon in the formulation of artificial neural networks make them more prevailing. Several benchmark and real-life problems of classification and function-approximation are illustrated.
Rights:
http://creativecommons.org/publicdomain/mark/1.0/ and policy:public

2. A Neural Network Approach for Assessing the Relationship between Grip Strength and Hand Anthropometry

Creator:
Cakit, Erman, Durgun , Behice , and Cetik , Oya
Format:
bez média and svazek
Type:
model:article and TEXT
Subject:
hand dimensions, grip strength, artificial neural network, stepwise regression analysis, and sensitivy analysis
Language:
English
Description:
This study aimed to determine grip strength data for Turkish dentistry students and developed prediction models that allow: i) investigation of the relationship between grip strength and hand anthropometry using artificial neural networks (ANNs) and stepwise regression analysis, ii) prediction of the grip strength of Turkish dentistry students, and iii) assessment of the potential impact of hand anthropometric variables on grip strength. The study included 153 right-handed dentistry students, consisting of 81 males and 72 females. From 44 anthropometric and biomechanical measurements obtained from the right hands of the participants; five anthropometric measurements were selected for ANN and regression modeling using stepwise regression analysis. We included stepwise regression analysis results to assess the predictive power of the neural network approach, in comparison to a classical statistical approach. When the model accuracy was calculated based on the coefficient of determination (R2), the root mean squared error (RMSE) and the mean absolute error (MAE) values for each of the models, ANN showed greater predictive accuracy than regression analysis, as demonstrated by experimental results. For the best performing ANN model, the testing values of the models correlated well with actual values, with a coefficient of determination (R2) of 0.858. Using the best performing ANN model, sensitivity analysis was applied to determine the effects of hand dimensions on grip strength and to rank these dimensions in order of importance. The results suggest that the three most sensitive input variables are the forearm length, the hand breadth and the finger circumference at the first joint of digit 5 and that the ANNs are promising techniques for predicting hand grip strength based on hand breadth, finger breadth, hand length, finger circumference and forearm length.
Rights:
http://creativecommons.org/publicdomain/mark/1.0/ and policy:public

3. An ANN approaches on estimating earthquake performances of existing RC buildings

Creator:
Arslan, M. H., Ceylan, M., and Koyuncu, T.
Format:
bez média and svazek
Type:
model:article and TEXT
Subject:
Earthquake performance, reinforced concrete, and artificial neural network
Language:
English
Description:
This study aims at developing an artificial intelligence-based (ANN based) analytical method to analyze earthquake performances of the reinforced concrete (RC) buildings. In the scope of the present study, 66 real RC buildings with four to ten storeys were subject to performance analysis according to 19 parameters considered effective on the performance of RC buildings. In addition, the level of performance of these buildings in case of an earthquake was determined on the basis of the 4-grade performance levels specified in Turkish Earthquake Code-2007 (TEC-2007). Thus, an output performance data group was created for the analyzed buildings, in accordance with the input data. Thanks to the ANN-based fast evaluation algorithm mentioned above and developed within the scope of the proposed project study, it will be possible to make an economic and rapid evaluation of four to ten-storey RC buildings in Turkey with great accuracy (about 80%). Detection of post-earthquake performances of RC buildings in the scope of the present study will facilitate reaching important results in terms of buildings, which will be beneficial for Civil Engineers of Turkey and similar countries.
Rights:
http://creativecommons.org/publicdomain/mark/1.0/ and policy:public

4. An improved E-model using artificial neural network VoIP quality predictor

Creator:
AL-Akhras, Mousa, ALMomani, Iman, and Sleit, Azzam
Format:
bez média and svazek
Type:
model:article and TEXT
Subject:
Voice over IP, artificial neural network, speech quality, E-model, non-intrusive, voiced, unvoiced, perceptual evaluation of speech quality, packet loss, and subjective-free
Language:
English
Description:
Voice over Internet Protocol (VoIP) networks are an increasingly important field in the world of telecommunication due to many involved advantages and potential revenue. Measuring speech quality in VoIP networks is an important aspect of such networks for legal, commercial and technical reasons. The E-model is a widely used objective approach for measuring the quality as it is applicable to monitoring live-traffic, automatically and non-intrusively. The E-model suffers from several drawbacks. Firstly, it considers the effect of packet loss on the speech quality collectively without looking at the content of the speech signal to check whether the loss occurred in voiced or unvoiced parts of the signal. Secondly, it depends on subjective tests to calibrate its parameters, which makes it applicable to limited conditions corresponding to specific subjective experiments. In this paper, a solution is proposed to overcome these two problems. The proposed solution improves the accuracy of the E-model by differentiating between packet loss during speech and silence periods. It also avoids the need for subjective tests, which makes it extendable to new network conditions. The proposed solution is based on an Artificial Neural Networks (ANN) approach and is compared with the accurate Perceptual Evaluation of Speech Quality (PESQ) model and the original E-model to confirm its accuracy. Several experiments are conducted to test the effectiveness of the proposed solution on two well-known ITU-T speech codecs; namely, G.723.1 and G.729.
Rights:
http://creativecommons.org/publicdomain/mark/1.0/ and policy:public

5. Artificial neural network model for biosorption of methylene blue by dead leaves of Posidonia oceanica (L.) Delile

Creator:
Demir, Guleser K., Dural, Ulas Mehmet, Alyuruk , Hakan, and Cavas, Levent
Format:
bez média and svazek
Type:
model:article and TEXT
Subject:
Biosorption, modeling, artificial neural network, and Posidonia oceanica
Language:
English
Description:
In the present study, an alternative promising evaluation method was recommended for dead leaves of Posidonia oceanica (L.) Delile as an adsorbent for biosorption of Methylene Blue (MB). The data from batch experiments were modeled by using Artificial Neural Network (ANN). The optimal operation conditions for biosorption of MB by P. oceanica dead leaves were found for pH, adsorbent dosage, temperature and initial dye concentration as 6, 0.3 g, 303 K and 50 mg/L, respectively. The adsorption reached equilibrium after 30 minutes. According to the results of sensitivity analysis, relative importance of temperature, dye concentration, pH, adsorbent dosage and process time on the biosorption of MB were 33%, 27%, 21%, 10% and 8%, respectively. Minimum mean square error (MSE) was found as 0.0169 by ANN modeling. The present study reveals a novel strategy for adsorption studies to utilize the highly accumulated biomass of dead leaves of P. oceanica in Turkish coastlines instead of burning these dead leaves.
Rights:
http://creativecommons.org/publicdomain/mark/1.0/ and policy:public

6. Combination of neural networks forecasters for monthly natural gas consumption prediction

Creator:
Kizilaslan, Recep and Karlik , Bekir
Format:
bez média and svazek
Type:
model:article and TEXT
Subject:
Enemy consumption, artificial neural network, and forecasting
Language:
English
Description:
This study presents different types of neural network algorithm based model forecasting gas consumption for residential and commercial consumers in Istanbul in Turkey. Using seven neural networks algorithms as forecasting models, we tried to find the best solution on forecasting of monthly natural gas consumption. These models were validated and tested on real monthly data from a distribution area covering two different regions of Anatolian and European sides in Istanbul. The analysis of results obtained for training and test sets show that the seven proposed artificial neural network models could be useful for the natural gas consumption forecast problem. It was shown that a conjugate gradient descent neural network model presented a more efficient solution than the other models.
Rights:
http://creativecommons.org/publicdomain/mark/1.0/ and policy:public

7. Computationally Simple Neural Network Approach to Determine Piecewise-Linear Dynamical Model

Creator:
Dolezel, P. and Heckenbergerova, J.
Format:
bez média and svazek
Type:
model:article and TEXT
Subject:
artificial neural network, momdeling, and nonlinear systems
Language:
English
Description:
The article introduces a new technique for nonlinear system modeling. This approach, in comparison to its alternatives, is straight and computationally undemanding. The article employs the fact that once a nonlinear problem is modeled by a piecewise-linear model, it can be solved by many efficient techniques. Thus, the result of introduced technique provides a set of linear equations. Each of the equations is valid in some region of state space and together, they approximate the whole nonlinear problem. The technique is comprehensively described and its advantages are demonstrated on an example.
Rights:
http://creativecommons.org/publicdomain/mark/1.0/ and policy:public

8. Homeostatic learning rule for artificial neural networks

Creator:
Ruzek, Martin
Format:
bez média and svazek
Type:
model:article and TEXT
Subject:
artificial neural network, learning rule, and biological neuron
Language:
English
Description:
This article presents an improvement of learning algorithm for an artificial neural network that makes the learning process more similar to a biological neuron, but still simple enough to be easily programmed. This idea is based on autonomous artificial neurons that are working together and at same time competing for resources; every neuron is trying to be better than the others, but also needs the feed back from other neurons. The proposed artificial neuron has similar forward signal processing as the standard perceptron; the main difference is the learning phase. The learning process is based on observing the weights of other neurons, but only in biologically plausible way, no back propagation of error or 'teacher' is allowed. The neuron is sending the signal in a forward direction into the higher layer, while the information about its function is being propagated in the opposite direction. This information does not have the form of energy, it is the observation of how the neuron's output is accepted by the others. The neurons are trying to 2nd such setting of their internal parameters that are optimal for the whole network. For this algorithm, it is necessary that the neurons are organized in layers. The tests proved the viability of this concept { the learning process is slower; but has other advantages, such as resistance against catastrophic interference or higher generalization
Rights:
http://creativecommons.org/publicdomain/mark/1.0/ and policy:public

9. Hybrid neural network-particle swarm algorithm to describe chaotic time series

Creator:
Lazzús, Juan A., Salfate, Ignacio, and Montecinos, Sonia
Format:
bez média and svazek
Type:
model:article and TEXT
Subject:
Chaotic time series, time series prediction, Mackey-Glass series, artificial neural network, and particle swarm optimization
Language:
English
Description:
An artificial neural network (ANN) based on particle swarm optimization (PSO) was developed for the time series prediction. This hybrid ANN+PSO algorithm was applied on Mackey-Glass series in the short-term prediction x(t+6) and the long-term prediction x(t+84), from the current value x(t) and the past values: x(t-6), x(t-12), x(t-18). Four cases were studied, alternating the timedelay parameter as 17 or 30. Also, the first four largest Lyapunov exponents were obtained for different time-delay. Simulation shows that this ANN+PSO method is a very powerful tool for making prediction of chaotic time series.
Rights:
http://creativecommons.org/publicdomain/mark/1.0/ and policy:public

10. Improved higher lead time river flow forecasts using sequential neural network with error updating

Creator:
Prakash, Om, Sudheer, K.P., and Srinivasan, K.
Format:
bez média and svazek
Type:
model:article and TEXT
Subject:
river flow forecasting, forecast lead time, error updating, artificial neural network, and genetic algorithm
Language:
Slovak
Description:
This paper presents a novel framework to use artificial neural network (ANN) for accurate forecasting of river flows at higher lead times. The proposed model, termed as sequential ANN (SANN), is based on the heuristic that a mechanism that provides an accurate representation of physical condition of the basin at the time of forecast, in terms of input information to ANNs at higher lead time, helps improve the forecast accuracy. In SANN, a series of ANNs are connected sequentially to extend the lead time of forecast, each of them taking a forecast value from an immediate preceding network as input. The output of each network is modified by adding an expected value of error so that the residual variance of the forecast series is minimized. The applicability of SANN in hydrological forecasting is illustrated through three case examples: a hypothetical time series, daily river flow forecasting of Kentucky River, USA and hourly river flow forecasting of Kolar River, India. The results demonstrate that SANN is capable of providing accurate forecasts up to 8 steps ahead. A very close fit (>94% efficiency) was obtained between computed and observed flows up to 1 hour in advance for all the cases, and the deterioration in fit was not significant as the forecast lead time increased (92% at 8 steps ahead). The results show that SANN performs much better than traditional ANN models in extending the forecast lead time, suggesting that it can be effectively employed in developing flood management measures.
Rights:
http://creativecommons.org/licenses/by-nc-sa/4.0/ and policy:public