Current Issue : April - June Volume : 2019 Issue Number : 2 Articles : 5 Articles
The versatility of the neural network (NN) technique allows it to be successfully applied in many fields of science and to a great\nvariety of problems. For each problem or class of problems, a generic NN technique (e.g., multilayer perceptron (MLP)) usually\nrequires some adjustments, which often are crucial for the development of a successful application. In this paper, we introduce a\nNN application that demonstrates the importance of such adjustments;moreover, in this case, the adjustments applied to a generic\nNN technique may be successfully used in many other NN applications. We introduce a NN technique, linking chlorophyll â??aâ?\n(chl-a) variabilityâ??primarily driven by biological processesâ??with the physical processes of the upper ocean using a NN-based\nempirical biological model for chl-a. In this study, satellite-derived surface parameter fields, sea-surface temperature (SST) and\nsea-surface height (SSH), as well as gridded salinity and temperature profiles from 0 to 75m depth are employed as signatures\nof upper-ocean dynamics. Chlorophyll-a fields from NOAAâ??s operational Visible Imaging Infrared Radiometer Suite (VIIRS) are\nused, aswell asModerateResolution Imaging Spectroradiometer (MODIS) andSea-ViewingWide Field-of-ViewSensor (SeaWiFS)\nchl-a concentrations. Different methods of optimizing the NN technique are investigated. Results are assessed using the rootmean-\nsquare error (RMSE) metric and cross-correlations between observed ocean color (OC) fields and NN output. To reduce the\nimpact of noise in the data and to obtain a stable computation of the NN Jacobian, an ensemble of NN with different weights is\nconstructed. This study demonstrates that the NN technique provides an accurate, computationally cheapmethod to generate long\n(up to 10 years) time series of consistent chl-a concentration that are in good agreementwith chl-a data observed by different satellite\nsensors during the relevant period. The presented NN demonstrates a very good ability to generalize in terms of both space and\ntime. Consequently, the NN-based empirical biological model for chl-a can be used in oceanic models, coupled climate prediction\nsystems, and data assimilation systems to dynamically consider biological processes in the upper ocean....
To make recommendation on items from the user for historical user rating\nseveral intelligent systems are using. The most common method is Recommendation\nsystems. The main areas which play major roles are social networking,\ndigital marketing, online shopping and E-commerce. Recommender\nsystem consists of several techniques for recommendations. Here we used the\nwell known approach named as Collaborative filtering (CF). There are two\ntypes of problems mainly available with collaborative filtering. They are complete\ncold start (CCS) problem and incomplete cold start (ICS) problem. The\nauthors proposed three novel methods such as collaborative filtering, and artificial\nneural networks and at last support vector machine to resolve CCS as\nwell ICS problems. Based on the specific deep neural network SADE we can\nbe able to remove the characteristics of products. By using sequential active of\nusers and product characteristics we have the capability to adapt the cold start\nproduct ratings with the applications of the state of the art CF model, time\nSVD++. The proposed system consists of Netflix rating dataset which is used\nto perform the baseline techniques for rating prediction of cold start items.\nThe calculation of two proposed recommendation techniques is compared on\nICS items, and it is proved that it will be adaptable method. The proposed\nmethod can be able to transfer the products since cold start transfers to\nnon-cold start status. Artificial Neural Network (ANN) is employed here to\nextract the item content features. One of the user preferences such as temporal\ndynamics is used to obtain the contented characteristics into predictions to\novercome those problems. For the process of classification we have used linear\nsupport vector machine classifiers to receive the better performance\nwhen compared with the earlier methods....
In this paper, a kind of BAM neural networks with leakage delays in the negative feedback terms and time-varying delays in\nactivation functions was considered. By constructing a suitable Lyapunov function and using inequality techniques, some sufficient\nconditions to ensure the existence and exponential stability of antiperiodic solutions of these neural networks were derived.These\nconditions extend some results recently appearing in recent papers. Lastly, an example is given to show the feasibility of these\nconditions....
This work reports a novel method by fusing Laplacian Eigenmaps feature conversion and\ndeep neural network (DNN) for machine condition assessment. Laplacian Eigenmaps is adopted to\ntransform data features from original high dimension space to projected lower dimensional space,\nthe DNN is optimized by the particle swarm optimization algorithm, and the machine run-to-failure\nexperiment were investigated for validation studies. Through a series of comparative experiments\nwith the original features, two other effective space transformation techniques, Principal Component\nAnalysis (PCA) and Isometric map (Isomap), and two other artificial intelligence methods, hidden\nMarkov model (HMM) as well as back-propagation neural network (BPNN), the present method in\nthis paper proved to be more effective for machine operation condition assessment....
We developed a new method of intelligent optimum strategy for a local coupled extreme\nlearning machine (LC-ELM). In this method, both the weights and biases between the input layer and\nthe hidden layer, as well as the addresses and radiuses in the local coupled parameters, are determined\nand optimized based on the particle swarm optimization (PSO) algorithm. Compared with extreme\nlearning machine (ELM), LC-ELM and extreme learning machine based on particle optimization\n(PSO-ELM) that have the same network size or compact network configuration, simulation results in\nterms of regression and classification benchmark problems show that the proposed algorithm, which\nis called LC-PSO-ELM, has improved generalization performance and robustness....
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