Water leakage forecasting: The application of a modified fuzzy evolving algorithm

This paper investigates the use of evolving fuzzy algorithms in forecasting. An evolving Takagi-Sugeno (eTS) algorithm, which is based on a recursive version of the subtractive algorithm is considered. It groups data into several clusters based on Euclidean distance between the relevant independent variables. The Mod eTS algorithm, which incorporates a modified dynamic update of cluster radii while accommodating new available data is proposed. The created clusters serve as a base for fuzzy If-Then rules with Gaussian membership functions which are defined using the cluster centres and have linear functions in the consequent i.e., Then parts of rules. The parameters of the linear functions are calculated using a weighted version of the Recursive Least Squares algorithm. The proposed algorithm is applied to a leakage forecasting problem faced by one of the leading UK water supplying companies. Using the real world data provided by the company the forecasting results obtained from the proposed modified eTS algorithm, Mod eTS, are compared to the standard eTS algorithm, exTS, eTS+ and fuzzy C-means clustering algorithm and some standard statistical forecasting methods. Different measures of forecasting accuracy are used. The results show higher accuracy achieved by applying the algorithm proposed compared to other fuzzy clustering algorithms and statistical methods. Similar results are obtained when comparing with other fuzzy evolving algorithms with dynamic cluster radii. Furthermore the algorithm generates typically a smaller number of clusters than standard fuzzy forecasting methods which leads to more transparent forecasting models

Forecasting Time-Series for NN GC1 using Evolving Takagi-Sugeno (eTS) Fuzzy Systems with On-line Inputs Selection

In this paper we present results and algorithm used to predict 14 days horizon from a number of time series provided by the NN GC1 concerning transportation datasets [1]. Our approach is based on applying the well known Evolving Takagi-Sugeno (eTS) Fuzzy Systems [2-6] to self-learn from the time series. ETS are characterized by the fact that they self-learn and evolve the fuzzy rule-based system which, in fact, represents their structure from the data stream on-line and in real-time mode. That means we used all the data samples from the time series only once, at any instant in time we only used one single input vector (which consist of few data samples as described below) and we do not iterate or memorize the whole sequence. It should be emphasized that this is a huge practical advantage which, unfortunately cannot be compared directly to the other competitors in NN GC1 if only precision/error is taken as a criteria. It is also worth to require time for calculations and memory usage as well as iterations and computational complexity to be provided and compared to build a fuller picture of the advantages the proposed technique offers. Nevertheless, we offer a computationally light and easy to use approach which in addition does not require any user-or problem-specific thresholds or parameters to be specified. Additionally, this approach is flexible in terms not only of its structure (fuzzy rule based and automatic self-development), but also in terms of automatic input selection as will be described below

Shortest Route at Dynamic Location with Node Combination-Dijkstra Algorithm

Abstract— Online transportation has become a basic requirement of the general public in support of all activities to go to work, school or vacation to the sights. Public transportation services compete to provide the best service so that consumers feel comfortable using the services offered, so that all activities are noticed, one of them is the search for the shortest route in picking the buyer or delivering to the destination. Node Combination method can minimize memory usage and this methode is more optimal when compared to A* and Ant Colony in the shortest route search like Dijkstra algorithm, but can’t store the history node that has been passed. Therefore, using node combination algorithm is very good in searching the shortest distance is not the shortest route. This paper is structured to modify the node combination algorithm to solve the problem of finding the shortest route at the dynamic location obtained from the transport fleet by displaying the nodes that have the shortest distance and will be implemented in the geographic information system in the form of map to facilitate the use of the system. Keywords— Shortest Path, Algorithm Dijkstra, Node Combination, Dynamic Location (key words