Bipolar fuzzy graphs

For the consideration of environmental aspects of the personal transportation, electric vehicle (EV) and plug-in hybrid electric vehicle (PHEV) has the prospective solution nonetheless there is an obstacle to access the charging system plug. Additionally, the charging system delivers its own troubles when we compared it with petrol station since the participation of the different charging alternative. For the last few years, the studies associated with optimization of PHEV/EV charging infrastructure have drawn the researcher׳s consideration. New challenges and chances for the growth of smart grid technologies depend upon the recently introduced electric vehicle charging infrastructure services. In this particular review study, the literature bearing on the use of optimization methods for charging infrastructure is considered.

Review of recent trends in optimization techniques for plug-in hybrid, and electric vehicle charging infrastructures

Application of a PSO-based neural network in analysis of outcomes of construction claims

Asynchronous spiking neural P systems with local synchronization

Pollution evaluation in streams using water quality indices: A case study from Turkey's Sapanca Lake Basin

The availability of accurate empirical models for multi-step-ahead (MS) prediction is desirable in many areas. Some ANN technologies, such as multiple-neural network, time-delay neural network (TDNN), and adaptive time-delay neural network (ATNN), have proven successful in addressing various complicated problems. The purpose of this study was to investigate the applicability of neural network MS predictive models. Motivated by the above-mentioned technologies, we proposed a hybrid neural network model, which integrated characteristics decomposition units, and a dynamic spline interpolation unit into the multiple ATNNs. Inside the net, the regular and certain information were extracted to ATNN, while both time delays and weights were dynamically adapted. The yearly average of the sunspots, which has been considered by geophysicists, environment scientists, and climatologists as a complicated non-linear system, was selected to test the hybrid model. Comparative results were presented between a traditional MS predictive model based on TDNN and the proposed model. Validation studies indicated that the proposed model is quite effective in MS prediction, especially for single-factor time series.

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Jing Xin Xie

Papers

A hybrid adaptive time-delay neural network model for multi-step-ahead prediction of sunspot activity