A New Decision Making Method for Selection of Optimal Data Using the Von Neumann-Morgenstern Theorem

The quality of the input data is amongst the decisive factors affecting the speed and effectiveness of recurrent neural network (RNN) learning. We present here a novel methodology to select optimal training data (those with the highest learning capacity) by approaching the problem from a decision making point of view. The key idea, which underpins the design of the mathematical structure that supports the selection, is to define first a binary relation that gives preference to inputs with higher estimator abilities. The Von Newman Morgenstern theorem (VNM), a cornerstone of decision theory, is then applied to determine the level of efficiency of the training dataset based on the probability of success derived from a purpose-designed framework based on Markov networks. To the best of the author’s knowledge, this is the first time that this result has been applied to data selection tasks. Hence, it is shown that Markov Networks, mainly known as generative models, can successfully participate in discriminative tasks when used in conjunction with the VNM theorem. The simplicity of our design allows the selection to be carried out alongside the training. Hence, since learning progresses with only the optimal inputs, the data noise gradually disappears: the result is an improvement in the performance while minimising the likelihood of overfitting

Adaptive water demand forecasting for near real-time management of smart water distribution systems

This paper presents a novel methodology to perform adaptive Water Demand Forecasting (WDF) for up to 24h ahead with the aim to support near real-time operational management of smart Water Distribution Systems (WDSs). The novel WDF methodology is exclusively based on the analysis of water demand time series (i.e., demand signals) and makes use of Evolutionary Artificial Neural Networks (EANNs). It is implemented in a fully automated, data-driven and self-learning Demand Forecasting System (DFS) that is readily transferable to practice. The main characteristics of the DFS are: (a) continuous adaptability to ever changing water demand patterns and (b) generic and seamless applicability to different demand signals. The DFS enables applying two alternative WDF approaches. In the first approach, multiple EANN models are used in parallel to separately forecast demands for different hours of the day. In the second approach, a single EANN model with a fixed forecast horizon (i.e., 1h) is used in a recursive fashion to forecast demands. Both approaches have been tested and verified on a real-life WDS in the United Kingdom (UK). The results obtained illustrate that, regardless of the WDF approach used, the novel methodology allows accurate forecasts to be generated thereby demonstrating the potential to yield substantial improvements to the state-of-the-art in near real-time WDS management. The results obtained also demonstrate that the multiple-EANN-models approach slightly outperforms the single-EANN-model approach in terms of WDF accuracy. The single-EANN-model approach, however, still enables achieving good WDF performance and may be a preferred option in engineering practice as it is easier to setup/implement. © 2014 Elsevier Ltd.UK Engineering and Physical Sciences Research Counci

Evolving neural networks for static single-position automated trading

This paper presents an approach to single-position, intraday automated trading based on a neurogenetic algorithm. An artificial neural network is evolved to provide trading signals to a simple automated trading agent. The neural network uses open, high, low, and close quotes of the selected financial instrument from the previous day, as well as a selection of the most popular technical indicators, to decide whether to take a single long or short position at market open. The position is then closed as soon as a given profit target is met or at market close. Experimental results indicate that, despite its simplicity, both in terms of input data and in terms of trading strategy, such an approach to automated trading may yield significant returns

Improving performance of genetic algorithms by using novel fitness functions

This thesis introduces Intelligent Fitness Functions and Partial Fitness Functions both of which can improve the performance of a genetic algorithm which is limited to a fixed run time. An Intelligent Fitness Function is defined as a fitness function with a memory. The memory is used to store information about individuals so that duplicate individuals do not need to have their fitness tested. Different types of memory (long and short term) and different storage strategies (fitness based, time base and frequency based) have been tested. The results show that an intelligent fitness function, with a time based long term memory improves the efficiency of a genetic algorithm the most. A Partial Fitness Function is defined as a fitness function that only partially tests the fitness of an individual at each generation. Thus only promising individuals get fully tested. Using a partial fitness function gives the genetic algorithm more evolutionary steps in the same length of time as a genetic algorithm using a normal fitness function. The results show that a genetic algorithm using a partial fitness function can achieve higher fitness levels than a genetic algorithm using a normal fitness function. Finally a genetic algorithm designed to solve a substitution cipher is compared to one equipped with an intelligent fitness function and another equipped with a partial fitness function. The genetic algorithm with the intelligent fitness function and the genetic algorithm with the partial fitness function both show a significant improvement over the genetic algorithm with a conventional fitness function.EThOS - Electronic Theses Online ServiceGBUnited Kingdo