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M. Javaid, M. Abbas, Jia-Bao Liu, W. C. Teh and Jinde Cao

Abstract

A topological property or index of a network is a numeric number which characterises the whole structure of the underlying network. It is used to predict the certain changes in the bio, chemical and physical activities of the networks. The 4-layered probabilistic neural networks are more general than the 3-layered probabilistic neural networks. Javaid and Cao [Neural Comput. and Applic., DOI 10.1007/s00521-017-2972-1] and Liu et al. [Journal of Artificial Intelligence and Soft Computing Research, 8(2018), 225-266] studied the certain degree and distance based topological indices (TI’s) of the 3-layered probabilistic neural networks. In this paper, we extend this study to the 4-layered probabilistic neural networks and compute the certain degree-based TI’s. In the end, a comparison between all the computed indices is included and it is also proved that the TI’s of the 4-layered probabilistic neural networks are better being strictly greater than the 3-layered probabilistic neural networks.

Open access

Ryotaro Kamimura

Abstract

The present paper1 aims to propose a new type of information-theoretic method to maximize mutual information between inputs and outputs. The importance of mutual information in neural networks is well known, but the actual implementation of mutual information maximization has been quite difficult to undertake. In addition, mutual information has not extensively been used in neural networks, meaning that its applicability is very limited. To overcome the shortcoming of mutual information maximization, we present it here in a very simplified manner by supposing that mutual information is already maximized before learning, or at least at the beginning of learning. The method was applied to three data sets (crab data set, wholesale data set, and human resources data set) and examined in terms of generalization performance and connection weights. The results showed that by disentangling connection weights, maximizing mutual information made it possible to explicitly interpret the relations between inputs and outputs.

Open access

Parisa Rastin, Basarab Matei, Guénaël Cabanes, Nistor Grozavu and Younès Bennani

Abstract

Collaborative Clustering is a data mining task the aim of which is to use several clustering algorithms to analyze different aspects of the same data. The aim of collaborative clustering is to reveal the common underlying structure of data spread across multiple data sites by applying clustering techniques. The idea of collaborative clustering is that each collaborator shares some information about the segmentation (structure) of its local data and improve its own clustering with the information provided by the other learners. This paper analyses the impact of the quality and the diversity of the potential learners to the quality of the collaboration for topological collaborative clustering algorithms based on the learning of a Self-Organizing Map (SOM). Experimental analysis on real data-sets showed that the diversity between learners impact the quality of the collaboration. We also showed that some internal indexes of quality are a good estimator of the increase of quality due to the collaboration.

Open access

Michal Mizera, Pawel Nowotarski, Aleksander Byrski and Marek Kisiel-Dorohinicki

Abstract

Evolutionary Multi-agent System introduced by late Krzysztof Cetnarowicz and developed further at the AGH University of Science and Technology became a reliable optimization system, both proven experimentally and theoretically. This paper follows a work of Byrski further testing and analyzing the efficacy of this metaheuristic based on popular, high-dimensional benchmark functions. The contents of this paper will be useful for anybody willing to apply this computing algorithm to continuous and not only optimization.

Open access

Md Meftahul Ferdaus, Sreenatha G. Anavatti, Matthew A. Garratt and Mahardhika Pratama

Abstract

Advanced and accurate modelling of a Flapping Wing Micro Air Vehicle (FW MAV) and its control is one of the recent research topics related to the field of autonomous MAVs. Some desiring features of the FW MAV are quick flight, vertical take-off and landing, hovering, and fast turn, and enhanced manoeuvrability contrasted with similar-sized fixed and rotary wing MAVs. Inspired by the FW MAV’s advanced features, a four-wing Nature-inspired (NI) FW MAV is modelled and controlled in this work. The Fuzzy C-Means (FCM) clustering algorithm is utilized to construct the data-driven NIFW MAV model. Being model free, it does not depend on the system dynamics and can incorporate various uncertainties like sensor error, wind gust etc. Furthermore, a Takagi-Sugeno (T-S) fuzzy structure based adaptive fuzzy controller is proposed. The proposed adaptive controller can tune its antecedent and consequent parameters using FCM clustering technique. This controller is employed to control the altitude of the NIFW MAV, and compared with a standalone Proportional Integral Derivative (PID) controller, and a Sliding Mode Control (SMC) theory based advanced controller. Parameter adaptation of the proposed controller helps to outperform it static PID counterpart. Performance of our controller is also comparable with its advanced and complex counterpart namely SMC-Fuzzy controller.

Open access

Yuzheng Lu, Yang Wang and Weidong Gao

Abstract

In this study, the wicking properties of ring and compact-siro ring spun staple yarns were compared. The twist level, which is related to the structure of the staple yarns, was found to significantly influence the wicking property of the two kinds of yarn. Polyester staple fibers with 1.33 dtex × 38 mm were selected as the staple fiber material, and the effect of the twist level on the wicking property was investigated using the capillary rise method. The results show that with a decreasing twist coefficient, the wicking height increases with a decrease in yarn compactness. The compact-siro spun yarn showed better wicking properties owing to it special ply yarn structure. Furthermore, the tension property of the yarns decreased significantly with a decrease in the twist coefficient. Compact-siro spinning was carried out to obtain staple yarns with lower twist coefficients, and the yarns showed great improvement in terms of yarn strength, fiber straightness, and wicking properties. Thus, compact-siro spinning is an efficient method to improve the wicking properties of staple yarns.

Open access

Xuan Luo, Gaoming Jiang and Honglian Cong

Abstract

This paper focuses on the better performance between the garment simulation result and the simulation speed. For simplicity and clarity, a notation “PART” is defined to indicate the areas between the garment and the human body satisfying some constraints. The discrete mechanical model can be achieved by the two-stage process. In the first stage, the garment can be divided into several PARTs constrained by the distance. In the second stage, the mechanical model of each PART is formulated with a mathematical expression. Thus, the mechanical model of the garment can be obtained. Through changing the constrained distance, the simulation result and the simulation speed can be observed. From the variable distance, a desired value can be chosen for an optimal value. The results of simulations and experiments demonstrate that the better performance can be achieved at a higher speed by saving runtime with the acceptable simulation results and the efficiency of the proposed scheme can be verified as well.

Open access

Massimiliano Zappa, Ladislav Holko, Martin Šanda, Tomáš Vitvar and Juraj Parajka