Integrated Neural Based System for State Estimation and Confidence Limit Analysis in Water Networks

In this paper a simple recurrent neural network (NN) is used as a basis for constructing an integrated system capable of finding the state estimates with corresponding confidence limits for water distribution systems. In the first phase of calculations a neural linear equations solver is combined with a Newton-Raphson iterations to find a solution to an overdetermined set of nonlinear equations describing water networks. The mathematical model of the water system is derived using measurements and pseudomeasurements consisting certain amount of uncertainty. This uncertainty has an impact on the accuracy to which the state estimates can be calculated. The second phase of calculations, using the same NN, is carried out in order to quantify the effect of measurement uncertainty on accuracy of the derived state estimates. Rather than a single deterministic state estimate, the set of all feasible states corresponding to a given level of measurement uncertainty is calculated. The set is presented in the form of upper and lower bounds for the individual variables, and hence provides limits on the potential error of each variable. The simulations have been carried out and results are presented for a realistic 34-node water distribution network

Neural Simulation of Water Systems for Efficient State Estimation

This paper presents a neural network based technique for the solution of a water system state estimation problem.The technique combines a neural linear equations solver with a Newton-Raphson iterations to obtain a solution to an overdetermined set of nonlinear equations. The algorithm has been applied to a realistic 34-node water network. By changing the values of neural network parameters both the least squares (LS) and least absolute values (LAV) estimates have been obtained and assessed with respect to their sensitivity to measurement errors

Simulation of Water Distribution Systems

In this paper a software package offering a means of simulating complex water distribution systems is described. It has been developed in the course of our investigations into the applicability of neural networks and fuzzy systems for the implementation of decision support systems in operational control of industrial processes with case-studies taken from the water industry. Examples of how the simulation package have been used in a design and testing of the algorithms for state estimation, confidence limit analysis and fault detection are presented. Arguments for using a suitable graphical visualization techniques in solving problems like meter placement or leakage diagnosis are also given and supported by a set of examples

General fuzzy min-max neural network for clustering and classification

This paper describes a general fuzzy min-max (GFMM) neural network which is a generalization and extension of the fuzzy min-max clustering and classification algorithms of Simpson (1992, 1993). The GFMM method combines supervised and unsupervised learning in a single training algorithm. The fusion of clustering and classification resulted in an algorithm that can be used as pure clustering, pure classification, or hybrid clustering classification. It exhibits a property of finding decision boundaries between classes while clustering patterns that cannot be said to belong to any of existing classes. Similarly to the original algorithms, the hyperbox fuzzy sets are used as a representation of clusters and classes. Learning is usually completed in a few passes and consists of placing and adjusting the hyperboxes in the pattern space; this is an expansion-contraction process. The classification results can be crisp or fuzzy. New data can be included without the need for retraining. While retaining all the interesting features of the original algorithms, a number of modifications to their definition have been made in order to accommodate fuzzy input patterns in the form of lower and upper bounds, combine the supervised and unsupervised learning, and improve the effectiveness of operations. A detailed account of the GFMM neural network, its comparison with the Simpson's fuzzy min-max neural networks, a set of examples, and an application to the leakage detection and identification in water distribution systems are given

Domain transformation approach to deterministic optimization of examination timetables

In this paper we introduce a new optimization method for the examinations scheduling problem. Rather than attempting direct optimization of assignments of exams to specific time-slots, we perform permutations of slots and reassignments of exams upon the feasible (but not optimal) schedules obtained by the standard graph colouring method with Largest Degree ordering. The proposed optimization methods have been evaluated on the University of Toronto, University of Nottingham and International Timetabling Competition (ITC2007) datasets. It is shown that the proposed method delivers competitive results compared to other constructive methods in the timetabling literature on both the Nottingham and Toronto datasets, and it maintains the same optimization pattern of the solution improvement on the ITC2007 dataset. A deterministic pattern obtained for all benchmark datasets, makes the proposed method more understandable to the users

Solving the randomly generated university examination timetabling problem through Domain Transformation Approach (DTA)

Amongst the wide-ranging areas of the timetabling problems, educational timetabling was reported as one of the most studied and researched areas in the timetabling literature. In this paper, our focus is the university examination timetabling. Despite many approaches proposed in the timetabling literature, it has been observed that there is no single heuristic that is able to solve a broad spectrum of scheduling problems because of the incorporation of problem-specific features in the heuristics. This observation calls for more extensive research and study into how to generate good quality schedules consistently. In order to solve the university examination timetabling problem systematically and efficiently, in our previous work, we have proposed an approach that we called a Domain Transformation Approach (DTA) which is underpinned by the insights from Granular Computing concept. We have tested DTA on some benchmark examination timetabling datasets, and the results obtained were very encouraging. Motivated by the previous encouraging results obtained, in this paper we will be analyzing the proposed method in different aspects. The objectives of this study include (1) To test the generality/applicability/universality of the proposed method (2) To compare and analyze the quality of the schedules generated by utilizing Hill Climbing (HC) optimization versus Genetic Algorithm (GA) optimization on a randomly generated benchmark. Based on the results obtained in this study, it was shown that our proposed DTA method has produced very encouraging results on randomly generated problems. Having said this, it was also shown that our proposed DTA method is very universal and applicable to different sets of examination timetabling problems

A constructive approach to examination timetabling based on adaptive decomposition and ordering

In this study, we investigate an adaptive decomposition and ordering strategy that automatically divides examinations into difficult and easy sets for constructing an examination timetable. The examinations in the difficult set are considered to be hard to place and hence are listed before the ones in the easy set in the construction process. Moreover, the examinations within each set are ordered using different strategies based on graph colouring heuristics. Initially, the examinations are placed into the easy set. During the construction process, examinations that cannot be scheduled are identified as the ones causing infeasibility and are moved forward in the difficult set to ensure earlier assignment in subsequent attempts. On the other hand, the examinations that can be scheduled remain in the easy set. Within the easy set, a new subset called the boundary set is introduced to accommodate shuffling strategies to change the given ordering of examinations. The proposed approach, which incorporates different ordering and shuffling strategies, is explored on the Carter benchmark problems. The empirical results show that the performance of our algorithm is broadly comparable to existing constructive approaches

Solving the preference-based conference scheduling problem through domain transformation approach

Conference scheduling can be quite a simple and straightforward problem if the number of papers to be scheduled is small.However, the problem can be very challenging and complex if the number of papers is large and various additional constraints need to be satisfied.Conference scheduling with regard to satisfying participants’ preferences can be understood as to generate schedule to minimize the clashes between slots or sessions that participants are interested to attend.Motivated by the current research trend in maximizing participants’ satisfactions, the study looks at the possibility of scheduling papers to sessions without any conflict by considering preferences by participants.In this research, preferences refer to the papers chosen by participants that they would like to attend its’ presentations sessions.Domain Transformation Approach (DTA), which has produced very encouraging results in our previous works, is used in this study to solve preference-based conference scheduling problem. The purpose of utilizing the method is to test the generality and universality of the approach in producing feasible schedule, given a different scheduling problem.The results obtained confirm that DTA efficiently generated feasible schedule which satisfies hard constraints and also fulfills all the preferences.With the generated schedule, all participants are able to attend their sessions of interest.In the future work, additional constraints will be taken into account in optimizing the schedules, for example balancing the number of papers assigned to each timeslot, and minimizing assignment of presenters to different timeslots.Other datasets could also be tested in order to test the generality of the proposed approach