Hierarchical Multilayer Perceptron Research Articles

An Intelligent Approach to Short-Term Wind Power Prediction Using Deep Neural Networks

Abstract In this paper, an intelligent approach to the Short-Term Wind Power Prediction (STWPP) problem is considered, with the use of various types of Deep Neural Networks (DNNs). The impact of the prediction time horizon length on accuracy, and the influence of temperature on prediction effectiveness have been analyzed. Three types of DNNs have been implemented and tested, including: CNN (Convolutional Neural Networks), GRU (Gated Recurrent Unit), and H-MLP (Hierarchical Multilayer Perceptron). The DNN architectures are part of the Deep Learning Prediction (DLP) framework that is applied in the Deep Learning Power Prediction System (DLPPS). The system is trained based on data that comes from a real wind farm. This is significant because the prediction results strongly depend on weather conditions in specific locations. The results obtained from the proposed system, for the real data, are presented and compared. The best result has been achieved for the GRU network. The key advantage of the system is a high effectiveness prediction using a minimal subset of parameters. The prediction of wind power in wind farms is very important as wind power capacity has shown a rapid increase, and has become a promising source of renewable energies.

A Hierarchical Approach for Point Cloud Classification With 3D Contextual Features

Classifying point cloud of urban landscapes plays essential roles in many urban applications. However, automating such a task is challenging due to irregular point distribution and complex urban scenes. Incorporating contextual information is crucial in improving classification accuracy of point clouds. In this article, we propose a hierarchical approach for point cloud classification with 3-D contextual features, which comprises three steps:segment-based classification with primitive features and a random forest classifier; extracting novel 3-D contextual features from the initial labels considering spatial relationships between neighboring segments and semantic dependencies; and refining classification with a combination of primitive features and spatial contextual features, and a hierarchical multilayer perceptron classifier that considers primitive features and spatial contextual features at different levels. The proposed method was tested on two point cloud datasets:the National University of Singapore (NUS) dataset and the Vaihingen benchmark dataset of the International Society of Photogrammetry and Remote Sensing. The evaluation results showed that the proposed method achieved an overall accuracy of 92.51% and 82.34% for the NUS dataset and Vaihingen dataset, respectively. The feature importance evaluation showed that 3-D spatial contextual features contributed useful information for discriminating different classes, such as roof, facade, grassland, tree, and ground. Quantitative comparisons further showed that the proposed method is more advantageous, especially in the detection of class roof and facade.

Improving Classification Accuracy of Heart Sound Signals Using Hierarchical MLP Network

Classification of heart sound signals to normal or their classes of disease are very important in screening and diagnosis system since various applications and devices that fulfilling this purpose are rapidly design and developed these days. This paper states and alternative method in improving classification accuracy of heart sound signals. Standard and improvised Multi-Layer Perceptron (MLP) network in hierarchical form were used to obtain the best classification results. Two data sets of normal and four abnormal heart sound signals from heart valve diseases were used to train and test the MLP networks. It is found that hierarchical MLP network could significantly increase the classification accuracy to 100% compared to standard MLP network with accuracy of 85.71% only.

Improved Bottleneck Feature using Hierarchical Deep Belief Networks for Keyword Spotting in Continues Speech

Bottleneck (BN) feature has attracted considerable attentions by its capacity of improving the accuracies in speech recognition tasks. Recently, researchers have proposed some modified approaches for extracting more effective BN feature, but these approaches still need further improvement. In this paper, motivated by both deep belief networks (DBN) and hierarchical Multilayer Perceptron (MLP), we propose hierarchical DBNs based BN feature and employed it for keyword spotting task. The hierarchical DBNs based BN feature is constructed with two DBNs in series which are sequentially trained. The first DBN outputs the posterior probabilities features, as well as the second DBN transforms the posterior probability features into a low dimensional representation with the information pertinent to classification through the BN layer. Experiments on hierarchical DBNs based BN feature is conducted with TIMIT dataset and using Point Process Model as the baseline system. Experimental results show that the hierarchical DBNs based BN feature is more robust and can achieve better accuracies than other features.