Search Results - normal distribution ((((factor algorithm) OR (means algorithm))) OR (function algorithm))

Optimal network reconfiguration and intelligent service restoration prediction technique based on Cuckoo search spring algorithm / Mohamad Izwan Zainal by Zainal, Mohamad Izwan

“…In addition, objective function using the same CSSA algorithm were applied i.e., Vmin and Ploss as the objective function, and multi-objective involves Vmin and Ploss as the objective function. …”
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A new variant of black hole algorithm based on multi population and levy flight for clustering problem by Haneen Abdul Wahab, Abdul Raheem

“…The obtained results were compared with the BH and previous optimization algorithms for both test functions as well as data clustering in terms of normal and high dimensional datasets. …”
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Enhancing clustering algorithm with initial centroids in tool wear region recognition by Kasim, Nur Adilla, Nuawi, Mohd Zaki, Abdul Ghani, Jaharah, Ngatiman, Nor Azazi, Che Haron, Che Hassan, Muhammad Rizal

“…Autonomous manufacturing allows the system to distinguish between a mild, normal and total failure in tool condition. K-means clustering has become the most applied algorithm in discovering classes in an unsupervised scenario. …”
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Streamflow prediction with large climate indices using several hybrid multilayer perceptrons and copula Bayesian model averaging by Panahi F., Ehteram M., Ahmed A.N., Huang Y.F., Mosavi A., El-Shafie A.

“…Climate models; Flood control; Floods; Forecasting; Information management; Inverse problems; Mean square error; Multilayer neural networks; Multilayers; Normal distribution; Particle swarm optimization (PSO); Reservoir management; Reservoirs (water); Risk management; Rivers; Stream flow; Uncertainty analysis; Bat algorithms; Bayesian model averaging; Bayesian modelling; Copula bayesian model; Gamma test; Inclusive multiple model; Multilayers perceptrons; Multiple-modeling; Natural hazard; Optimization algorithms; Bayesian networks; flood; flood control; North Atlantic Oscillation; perception; reservoir; streamflow; uncertainty analysis; Kelantan; Malaysia; West Malaysia…”

Automatic database of robust neural network forecasting / Saadi Ahmad Kamaruddin, Nor Azura Md. Ghani and Norazan Mohamed Ramli by Ahmad Kamaruddin, Saadi, Md. Ghani, Nor Azura, Mohamed Ramli, Norazan

“…The backpropagation algorithm is one of the most famous algorithms to train neural network based on the mean square error (MSE) of ordinary least squares (OLS). …”
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Peak pressure analysis of foot plantar distribution based on image processing algorithm by Sabry, Ali Hussein

“…The other main goal of this work is to create an algorithm which has the ability to formulate accurately and reliably the distribution of pressure over the foot plantar. …”
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Determining penetration limit of central distributed generation topology in radial distribution networks by Suliman, Mohamed Saad Abdelgadir

“…The beta probability density functions were used to model the photovoltaic generation, while the normal probability density functions were used to model the load demand. …”
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ETERS: A comprehensive energy aware trust-based efficient routing scheme for adversarial WSNs by Khan, T., Singh, K., Hasan, M.H., Ahmad, K., Reddy, G.T., Mohan, S., Ahmadian, A.

“…ETERS utilizes the Beta distribution-based trust function because recovery of trust values under attacks is faster in Beta distribution than Gaussian and Dirichlet distribution. …”
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Optimal placement and sizing of distributed generation in radial distribution networks using particle swarm optimization and forward backward sweep method by Lawal, Sani Mohammed

“…The proposed PSO algorithm is used to determine optimal placement and size of DG in radial distribution networks, where Forward Backward Sweep Method (FBSM) of distribution load flow analysis was used, to determine the actual power loss in the system. …”
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Efficiency of 4253HT smoothers in extracting signal from noise and their applications in forecasting by Khairol Azmi, Nurul Nisa'

“…The modified 4253HT using adaptive mean showed the most effective, compared to others, in extracting low, moderate and high frequency of sinusoidal signal from the noise with 10%, 25%, 50% and 75% contaminated normal distribution. …”
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Transient analysis for leak signature identification based on Hilbert Huang transform and integrated kurtosis algorithm for z-notch filter technique by Muhammad Hanafi, Yusop

“…The current research presents the implementation of an integrated kurtosisbased algorithm for a z-filter technique (Ikaz) to kurtosis ratio (Ikaz-kurtosis), for this allows automatic selection of the IMF that should be used. …”
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Research on the construction of an efficient and lightweight online detection method for tiny surface defects through model compression and knowledge distillation by Chen, Qipeng, Xiong, Qiaoqiao, Huang, Haisong, Tang, Saihong, Liu, Zhenghong

“…Finally, the concept of model compression is integrated, utilizing scaling factors in the batch normalization (BN) layer, and introducing sparse factors to perform sparse training on the network. …”
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Improved performance in distributed estimation by convex combination of DNSAF and DNLMS algorithms by Ahmad Pouradabi, Amir Rastegarnia, Azam Khalili, Ali Farzamnia

“…Diffusion normalized least mean square (DNLMS) algorithm has low misadjustment error, but it is slow in convergence. …”
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Reservoir Inflow Forecasting Using Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System Techniques by Googhari, Shahram Karimi

“…The models were trained with normalized and non-normalized data. The selected ANFIS model was trained with normalized data with 6 Gaussian membership functions for each of 9 inputs and 6 rules. …”
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Multi-Objective Hybrid Algorithm For The Classification Of Imbalanced Datasets by Saeed, Sana

“…For this purpose, the normal distributions are applied to each class. The parameters of this distribution are optimized by applying the proposed MOHA. …”
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Dynamic Robust Bootstrap Algorithm for Linear Model Selection Using Least Trimmed Squares by Uraibi, Hassan Sami

“…One of the important assumptions of the linear model is that the error terms are normally distributed. Unfortunately, many researchers are not aware that the performance of the OLS can be very poor when the data set that one often makes a normal assumption, has a heavy-tailed distribution which may arise as a result of the presence of outliers. …”
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Statistical approach on grading: mixture modeling by Md. Desa, Zairul Nor Deana

“…In the conditional Bayesian model, we assume the data to follow the Normal Mixture distribution where the grades are distinctively separated by the parameters: means and proportions of the Normal Mixture distribution. …”
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Robust Kernel Density Function Estimation by Dadkhah, Kourosh

“…To do this evaluation, the mixtures of bivariate normal distribution with different percentage of contribution are simulated. …”
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Determination of blood cholinesterase, neurobehavioral performance and genetic damage due to organophosphate exposure among male cocoa farmers in Pahang and Perak, Malaysia by Mohammad, Norsyazwani

“…Most of them were slightly exposed to pesticides, but some of them showed poor motor functionality and manual dexterity. There are other factors might contribute to poor motor functionality and manual dexterity such as the environment, genetics and their lifestyle. …”
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Entropy in portfolio optimization / Yasaman Izadparast Shirazi by Yasaman Izadparast, Shirazi

“…More specifically, we use multi-objective models that are the mean-entropy-entropy (MEE). The purpose of this new model is to overcome the limitations as observed in a traditional model; that is, having performance close to Markowitz’s mean-variance (MV) model when data comes from a normal distribution, but exhibit better performance when data comes from a non-normal distribution. …”
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