This paper presents a computational intelligence approach for coping with bullwhip effect in supply chains (SCs). An imperialist competitive algorithm (ICA) is employed to decrease the bullwhip effect and cost (inventory, ordering and production) in the MIT beer distribution game. The ICA is used to determine the optimal ordering policy for members of the SC. The results shows the ability of th...

The gradient based algorithms are the most basic independent component analysis (ICA) algorithms, used in Blind signal separation (BSS). Because these algorithms adopt fixed step size, the choice of step size affects the performance and the convergence speed of the algorithm. In this paper, we propose a new algorithm SCG-ICA for blind signal separation. The new algorithm significantly improves ...

the Dept. of Microelectronics and Computer Engineering, UTM, Malaysia and Signal Processing Research Center, QUT, Australia A Simple ICA Algorithm Based On Geometrical Approach. A. MANSOUR 1, C. G. PUNTONET 2 and N. OHNISHI 1;3 1BMC RIKEN, Anagahora, Shimoshidami, Moriyama-ku, Nagoya 463-0003 (JAPAN) 2Dept. of Architecture and Computer Technology.University of Granada. Granada 18071 (SPAIN) 3De...

Overcomplete blind source separation (BSS) tries to recover more sources from less sensor signals. We present a new approach based on an estimated histogram of the sensor data; we search for the points fulfilling the overcomplete Geometric Convergence Condition, which has been shown to be a limit condition of overcomplete geometric BSS [1]. The paper concludes with an example and a comparison o...

This paper evaluates the performance of some major ICA algorithms like Cardoso’s Joint Approximate Diagonalization of Eigen matrices (JADE), Bell and Sejnowski’s Infomax algorithm and Comon’s algorithm in a biomedical blind source separation problem. Independent signals representing Fetal ECG (FECG) and Maternal ECG (MECG) generated and then mixed linearly to simulate a recording of electrocard...

In this paper, an improved version of Principal Component Analysis (PCA) and Independent Component Analysis (ICA) is proposed for feature extraction to classify the ischemic beats from electrocardiogram (ECG) signal. The Fuzzy C-Means (FCM) and Genetic Algorithm (GA) is combined with PCA and ICA to extract more relevant features; the proposed methods are named as Fuzzy-Genetic based PCA (FGPCA)...

We propose a new two-stage blind separation and deconvolution (BSD) algorithm for a convolutive mixture of speech, in which a new Single-Input Multiple-Output (SIMO)-modelbased ICA (SIMO-ICA) and blind multichannel inverse filtering are combined. SIMO-ICA can separate the mixed signals, not into monaural source signals but into SIMO-model-based signals from independent sources as they are at th...

We present a new algorithm for nonlinear blind source separation, which is based on the geometry of the mixture space. This space is decomposed in a set of concentric rings, in which we perform ordinary linear ICA after central transformation; we show that this transformation can be left out if we use linear geometric ICA. In any case, we get a set of images of ring points under the original mi...

Abstract: This paper provides a new statistical approach to blind recovery of both earth signal and source wavelet given only the seismic traces using independent component analysis (ICA) by explicitly exploiting the sparsity of both the reflectivity sequence and the mixing matrix. Our proposed blind seismic deconvolution algorithm consists of three steps. Firstly, a transformation method that ...

A blind source separation algorithm is used to estimate the mixing operator from electromagnetic emission signals through independent component analysis (ICA) technique. The mixing operator contains important information about the sources’ features and about the electromagnetic field propagation phenomena, thus, by properly interpreting the results given by the ICA algorithm a blind source loca...