Functional Multi-Layer Perceptron: a Nonlinear Tool for Functional Data Analysis

In this paper, we study a natural extension of Multi-Layer Perceptrons (MLP) to functional inputs. We show that fundamental results for classical MLP can be extended to functional MLP. We obtain universal approximation results that show the expressive power of functional MLP is comparable to that of numerical MLP. We obtain consistency results which imply that the estimation of optimal parameters for functional MLP is statistically well defined. We finally show on simulated and real world data that the proposed model performs in a very satisfactory way.Comment: http://www.sciencedirect.com/science/journal/0893608

The impact of architecture on the performance of artificial neural networks

A number of researchers have investigated the impact of network architecture on the performance of artificial neural networks. Particular attention has been paid to the impact on the performance of the multi-layer perceptron of architectural issues, and the use of various strategies to attain an optimal network structure. However, there are still perceived limitations with the multi-layer perceptron and networks that employ a different architecture to the multi-layer perceptron have gained in popularity in recent years, particularly, networks that implement a more localised solution, where the solution in one area of the problem space does not impact, or has a minimal impact, on other areas of the space. In this study, we discuss the major architectural issues affecting the performance of a multi-layer perceptron, before moving on to examine in detail the performance of a new localised network, namely the bumptree. The work presented here examines the impact on the performance of artificial neural networks of employing alternative networks to the long established multi-layer perceptron. In particular, networks that impose a solution where the impact of each parameter in the final network architecture has a localised impact on the problem space being modelled are examined. The alternatives examined are the radial basis function and bumptree neural networks, and the impact of architectural issues on the performance of these networks is examined. Particular attention is paid to the bumptree, with new techniques for both developing the bumptree structure and employing this structure to classify patterns being examined

Multi-Scale U-Shape MLP for Hyperspectral Image Classification

Hyperspectral images have significant applications in various domains, since they register numerous semantic and spatial information in the spectral band with spatial variability of spectral signatures. Two critical challenges in identifying pixels of the hyperspectral image are respectively representing the correlated information among the local and global, as well as the abundant parameters of the model. To tackle this challenge, we propose a Multi-Scale U-shape Multi-Layer Perceptron (MUMLP) a model consisting of the designed MSC (Multi-Scale Channel) block and the UMLP (U-shape Multi-Layer Perceptron) structure. MSC transforms the channel dimension and mixes spectral band feature to embed the deep-level representation adequately. UMLP is designed by the encoder-decoder structure with multi-layer perceptron layers, which is capable of compressing the large-scale parameters. Extensive experiments are conducted to demonstrate our model can outperform state-of-the-art methods across-the-board on three wide-adopted public datasets, namely Pavia University, Houston 2013 and Houston 2018Comment: 5 page

Klasifikasi Wajah Manusia Menggunakan Multi Layer Perceptron

The problem of data security at a time when it is needed in the world of technology. The use of biometrics as data security is very necessary. This study aims to detect human biometrics using the Kinect sensor. The biometric that is detected is the face. The face image is captured by the Kinect sensor. For data feature extraction using Gray Level Co-Occurrence Matrix (GLCM. The parameters used are Contrast, Energy, Homogenity, and Correlation. The data obtained will be classified using Multi Layer Perceptron. Face classification is based on race. There are 3 races studied namely Indonesian, Chinese and African Native Races. The total data used are 100 photos of faces. The classification results show an accuracy of 86.7% using Multi Layer PerceptronPermasalahan keamanan data pada saat sangat dibutuhkan dalam dunia teknologi. Penggunaan biometrik sebagai pengamanan data sangat diperlukan. Penelitian ini bertujuan untuk mendeteksi biometrik manusia menggunakan sensor Kinect. Adapun biometric yang dideteksi adalah wajah. Hasil citra wajah ditangkap oleh sensor Kinect. Untuk ekstraksi fitur data menggunakan Gray Level Co-Occurrence Matrix (GLCM. Adapun parameter yang digunakan adalah Contrast, Energy, Homogenity, dan Correlation. Data yang diperoleh akan diklasifikasikan menggunakan Multi Layer Perceptron. Pengklasifikasian wajah dilakukan berdasarkan ras. Terdapat 3 ras yang di teliti yaitu Ras Asli Indonesia, Chinese dan Afrika. Total data yang digunakan sebanyak 100 foto wajah. Hasil klasifikasi menunjukkan akurasi sebesar 86,7 % menggunakan Multi Layer Perceptro

Tourism demand forecasting with neural network models : Different ways of treating information

This paper aims to compare the performance of three different artificial neural network techniques for tourist demand forecasting: a multi-layer perceptron, a radial basis function and an Elman network. We find that multi-layer perceptron and radial basis function models outperform Elman networks. We repeated the experiment assuming different topologies regarding the number of lags used for concatenation so as to evaluate the effect of the memory on the forecasting results. We find that for higher memories, the forecasting performance obtained for longer horizons improves, suggesting the importance of increasing the dimensionality for long-term forecasting

Incremental Learning Approach for Enhancing the Performance of Multi-Layer Perceptron for Determining the Stock Trend

This paper introduces a new technique for achieving minimum risk of predicting stock trend using multi-layer perceptron. The proposed technique presents the method of classification the stock trend .the paper show a comparison among multi-layer perceptron, gene learning theory. The achieved results show the superior performance of the multi-layer perceptron which is based on mathematical back ground