For a learning model to be effective in online modeling of nonstationary data, it must not only be equipped with high adaptability to track the changing data dynamics but also maintain low complexity to meet online computational restrictions. Based on these two important principles, in this paper, we propose a fast adaptive gradient radial basis function (GRBF) network for nonlinear and nonstationary time series prediction. Specifically, an initial compact GRBF model is constructed on the training data using the orthogonal least squares algorithm, which is capable of modeling variations of local mean and trend in the signal well. During the online operation, when the current model does not perform well, the worst performing GRBF node is replaced by a new node, whose structure is optimized to fit the current data. Owing to the local one-step predictor property of GRBF node, this adaptive node replacement can be done very efficiently. Experiments involving two chaotic time series and two real-world signals are used to demonstrate the superior online prediction performance of the proposed fast adaptive GRBF algorithm over a range of benchmark schemes, in terms of prediction accuracy and real-time computational complexity.

Article:Cheng, T., Chen, M., Fleming, P.J. orcid.org/0000-0001-9837-8404 et al. (2 more authors) (2017) A novel hybrid teaching learning based multi-objective particle swarm optimization. Neurocomputing, Abstract:How to obtain a good convergence and well-spread optimal Pareto front is still a major challenge for most meta-heuristic multi-objective optimization (MOO) methods. In this paper, a novel hybrid teaching learning based particle swarm optimization (HTL-PSO) with circular crowded sorting (CCS), named HTL-MOPSO, is proposed for solving MOO problems. Specifically, the new HTL-MOPSO combines the canonical PSO search with a teaching-learning-based optimization (TLBO) algorithm in order to improve search ability and speed up search procedure. Also, CCS technique is developed to improve the diversity and spread of solutions when truncating the external elitism archive. The performance of HTL-MOPSO algorithm was tested on several well-known benchmarks problems and compared with other state-of-the-art MOO algorithms in respect of convergence and spread of final solutions to the true Pareto front. Also, the individual contributions made by the strategies of HTL-PSO and CCS are analyzed. Experimental results validate the effectiveness of HTL-MOPSO and demonstrate its superior ability to find solutions of better spread and diversity, while assuring a good convergence.

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Yangtze River is one of the world's most important cargo-carrying rivers. However, the traffic capacity is becoming the bottleneck for further developments. This has been highlighted in recent Yangtze River economic zone proposal in which the improvement of the Yangtze River traffic capacity is a key project. Efficient traffic management based on ships' trajectory length prediction is a key way to improve the traffic capacity. Yet, in existing intelligent traffic signalling systems (ITSSs), ships are supposed to travel exactly along the central line of the Yangtze River which is often not a valid assumption and has caused a number of problems. Over the past few years, traffic data have been accumulated exponentially, leading to the big data era. This trend allows more accurate prediction of ships' travel trajectory length based on historical data. In this paper, ships' historical trajectories are first grouped by using the Fuzzy C-Means clustering algorithm. The relationship between some known factors (i.e. ship speed, loading capacity, self-weight, maximum power, ship length, ship width, ship type and water level) and the resultant memberships are then modeled using Artificial Neural Networks (ANN). The trajectory length is then estimated by the sum of the predicted probabilities multiplied by the trajectory cluster centers' length. The experimental results show that the proposed method can reduce the probability of generating wrong traffic control signals by 89% over existing ITSSs. This will significantly improve the efficiency of the Yangtze river traffic management system, and increase the traffic capacity by reducing the travelling time. Index Terms-trajectory prediction, data driven, fuzzy cmeans (FCM), artificial neural networks (ANN), intelligent traffic signalling system (ITSS).

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This is a repository copy of Missing Data Imputation with OLS-based Autoencoder for Intelligent Manufacturing.

To address the global challenge of the climate change, more strict legislations worldwide on carbon emission reductions have put energy intensive industries under immense pressure to improve the energy efficiency. Due to the lack of technical support and financial incentives, a range of technical and economic barriers still exist for small-medium enterprises (SMEs). This paper first introduces a point energy technology, which is developed for SMEs to improve the insight of the energy usage in the manufacturing processes and installed in a local bakery. Statistical analysis of electricity consumption data over a sevenday period is conducted, including the identification of operational modes for individual processing units using an enhanced clustering method and the voltage unbalance conditions associated with these identified modes. Two technical strategies, namely electrical load allotment and voltage unbalance minimisation, are then proposed, which could attain more than 800 kwh energy saving during this period and the current unbalance could be reduced to less than 10%. In

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