Neural Network World; International Journal on Neural and Mass - Parallel Computing and Information Systems

Threshold Voltage Modeling Using Neural Networks*

In this paper, threshold voltage modeling based on neural networks is presented. The database was obtained by performing DC analysis with possible combinations of MOSFETs terminal voltages and channel widths which directly effect threshold voltage values in submicron technology. The neural network was trained with the database including 0.25 µm and 0.40 µm TSMC process parameters. In order to prove the extrapolation ability, the test dataset is constituted with 0.18 µm TSMC process parameters...

Neural Networks Training by Artificial Bee Colony Algorithm On Pattern Classification

Artificial Neural Networks are commonly used in pattern classification, function approximation, optimization, pattern matching, machine learning and associative memories. They are currently being an alternative to traditional statistical methods for mining data sets in order to classify data. Artificial Neural Networks are well-established technology for solving prediction and classification problems, using training and testing data to build a model. However, the success of the networks is hi...

Multimodal Affect Detection of Car Drivers

The affective state of car drivers has a great impact on their driving behavior. In case of a high stress level, car drivers take more risk in turn over, car following and in neglecting traffic warning signs. In the paper we present first a list of factors which have an impact on the psychological state of the car driver with a focus on the affective state of car drivers. Next we give an outline of our multimodal system to assess the affective state of car drivers by analysis of facial expres...

Hybrid Model for Multi-Stop Arrival Time Prediction

Forecasting arrival times of a vehicle at many downstream stops is very important in many cases. For multi-stop arrival time prediction, direct approaches and iterative approaches possess respective merits. Therefore, a hybrid method that has both direct and iterative modeling abilities is presented to forecast arrival times at multiple stops. The hybrid method consists of an iterative support vector machine (SVM)-based prediction model and a direct SVM-based prediction model. In hybrid model...