In spatiotemporal data commonly encountered in geographical systems, biomedical signals, and the like, each datum is composed of features comprising a spatial component and a temporal part. Clustering of data of this nature poses challenges, especially in terms of a suitable treatment of the spatial and temporal components of the data. In this study, proceeding with the objective function-based clustering (such as, e.g., fuzzy C-means), we revisit and augment the algorithm to make it applicable to spatiotemporal data. An augmented distance function is discussed, and the resulting clustering algorithm is provided. Two optimization criteria, i.e., a reconstruction error and a prediction error, are introduced and used as a vehicle to optimize the performance of the clustering method. Experimental results obtained for synthetic and real-world data are reported.
This paper presents a hybrid fuzzy wavelet neural network (HFWNN) realized with the aid of polynomial neural networks (PNNs) and fuzzy inference-based wavelet neurons (FIWNs). Two types of FIWNs including fuzzy set inference-based wavelet neurons (FSIWNs) and fuzzy relation inference-based wavelet neurons (FRIWNs) are proposed. In particular, a FIWN without any fuzzy set component (viz., a premise part of fuzzy rule) becomes a wavelet neuron (WN). To alleviate the limitations of the conventional wavelet neural networks or fuzzy wavelet neural networks whose parameters are determined based on a purely random basis, the parameters of wavelet functions standing in FIWNs or WNs are initialized by using the C-Means clustering method. The overall architecture of the HFWNN is similar to the one of the typical PNNs. The main strategies in the design of HFWNN are developed as follows. First, the first layer of the network consists of FIWNs (e.g., FSIWN or FRIWN) that are used to reflect the uncertainty of data, while the second and higher layers consist of WNs, which exhibit a high level of flexibility and realize a linear combination of wavelet functions. Second, the parameters used in the design of the HFWNN are adjusted through genetic optimization. To evaluate the performance of the proposed HFWNN, several publicly available data are considered. Furthermore a thorough comparative analysis is covered.
Multiagent systems are inherently associated with their distributivity, which enforces a great deal of communication mechanisms. To effectively arrive at meaningful solutions in a vast array of problem-solving tasks, it becomes imperative to establish a sound machinery of reconciling findings which might form partial solutions to an overall problem. In this paper, we focus on a broad category of problems of collaborative data analysis realized by a collection of agents having access to their individual data and exchanging findings through their collaboration activities. Such problems of data analysis arise in the context of building a global view at a certain phenomenon (process) by viewing it from different perspectives (and thus engaging various collections of attributes by various agents). Our goal is to develop some interaction between the agents so that they could form an overall perspective, where the knowledge available locally is shared and reconciled. The underlying format of knowledge built by the agents is that of information granules and fuzzy sets in particular. We develop a comprehensive optimization scheme and discuss its two-phase nature in which the communication phase of the granular findings intertwines with the local optimization being realized by the agents at the level of the individual datasite and exploits the evidence collected from other sites. We show how the mechanism of fuzzy granulation realized in the form of a well-known fuzzy c-means (FCM) clustering can be augmented to support collaborative activities required by the agents. For this purpose, we introduce augmented versions of the original objective function used in the FCM and derive algorithmic details. We also discuss an issue of optimizing the strength of collaborative linkages, so that the reconciled findings attain the highest level of consistency (agreement). The presented experimental studies include some synthetic data and selected data sets coming from the Machine Learning repository.
In this work a new approach developed by using ordinal sums to apply general t-norms to inference systems of different neuro-fuzzy systems is proposed. A genetic algorithm based strategic to search the best t-norms and/or t-conorms from data is adopted. By using the approach two known neuro-fuzzy systems, that are the fuzzy basis function network and the fuzzy relation neural network models are compared. Several experiments on synthetic and benchmark data using different parametric and non-parametric t-norms and t-conorms are made.
From the common-sense viewpoint, if a person who weighs around 100 kilograms gains one more kilogram, his weight is still around 100 kilograms. Alas, not so in traditional fuzzy arithmetic. In this paper, we propose a modification of fuzzy arithmetic which does have this property. We gain the desired property, but there is no free lunch - we have to lose two important properties of the traditional fuzzy arithmetic: first, addition is no longer always associative; second, addition is no longer always easily computable.
We consider first the situation where one decision maker has to choose some alternatives among a set of given and predetermined alternatives in the presence of multiple attributes. This corresponds to multiple-attribute decision making (MADM).
A comprehensive design methodology of hybrid fuzzy controllers (HFC) is proposed. The HFC comes in a form of a convex combination of a standard PID controller and a fuzzy controller. The design procedure dwells on the use of evolutionary computing (genetic algorithm) and an auto‐tuning algorithm. The tuning of the scaling factors of the HFC is an essential component of the entire optimization process. A numerical study is presented and a detailed comparative analysis is also included.
In group decision making (GDM), there are seldom ideal scenarios that all the preference information given by all individuals reach a highly level of agreement. Conflicts are present in the information fusion process and decision makers (DMs) have to negotiate and reconcile differences. To address this issue, it becomes inevitable to consider intelligent GDM method. In this article, we propose the granular neural network (GNN) to realize the aggregation process from the perspective of granular computing and machine learning. Our study is involved in an extension of best–worst method to the GDM scenario. The procedure is outlined as follows: first, information granules are allocated around the prototype of individuals' preferences, complying with the principle of justifiable granularity. Thereby, the granular inputs are brought into a well-trained GNN. An adaptive particle swarm optimization algorithm is applied to optimize allocation of information granules. We calculate the threshold of consistency index for this granular model. Finally, a case study about hotel selection on Booking.com is presented to illustrate the performance of the proposed model. In addition, we use the stochastic analysis method to randomize the weights of group members with the objective to assess the robustness of the model. The feasibility and validity of the model are demonstrated by completing comparative analysis. The originality of this article is to establish a real data-driven granular GDM model both considering the optimization of group consistency and consensus.
