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Error-output recurrent multi-layer Kernel Reservoir Network for electricity load time series forecasting
(2023-01-01)
Liu, Zongying
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Tahir, Ghalib Ahmed
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Masuyama, Naoki
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Kakudi, Habeebah Adamu
;
Fu, Zhongyu
Electricity is one of the most consumed commodities in the modern world. Electricity load prediction models are used to plan distribution operations to balance the equilibrium of demand and supply. This necessity has increased the number of recent research works. They employed several learning algorithms, such as support vector regression, to predict demands. However, these algorithms have high computational cost and too many user-defined parameters that directly impact their performance. Recently, randomization-based learning algorithms have been widely tested because they performed well at a lower cost. However, still, there was a main drawback: uncertainty in approximation and learning. This work employed a kernel trick to solve the uncertainty problem. A kernel with reservoir-state layers was used to solve the problem. The kernel reservoir-state layers from the echo state network not only transformed features into high-dimensional space, but also enhanced the forecasting ability by learning temporal information. Additionally, the proposed model also had a multi-step prediction ability that used previous forecasting errors to update the output weights in the current step to prevent an accumulated error problem. We compared our proposed model with single-layer and multi-layer variants of Extreme Learning Machine, Echo State Network, and Random Vector Functional Link on ten electrical load data sets. The proposed model showed the best performance on 9/10 data sets in terms of Mean Square Error or Symmetric Mean Absolute Percentage Error. These findings implied that the proposed algorithm was superior in forecasting long-term electricity load.
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Diagnosing metabolic syndrome using genetically optimised Bayesian ARTMAP
(2019-01-01)
Kakudi, Habeebah Adamu
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Loo, Chu Kiong
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Moy, Foong Ming
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Masuyama, Naoki
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Pasupa, Kitsuchart
Metabolic Syndrome (MetS) constitutes of metabolic abnormalities that lead to non-communicable diseases, such as type II diabetes, cardiovascular diseases, and cancer. Early and accurate diagnosis of this abnormality is required to prevent its further progression to these diseases. This paper aims to diagnose the risk of MetS using a new non-clinical approach called 'genetically optimized Bayesian adaptive resonance theory mapping' (GOBAM). We evolve the Bayesian adaptive resonance theory mapping (BAM) by using genetic algorithm to optimize the parameters of BAM and its training input sequence. We use the GOBAM algorithm to classify individuals as either being at risk of MetS or not at risk of MetS with a related posterior probability, which ranges between 0 and 1. A data set of 11 237 Malaysians from the CLUSTer study stratified by age and gender into four subcategories was used to evaluate the proposed GOBAM algorithm. The comparative evaluation of our results suggested that the GOBAM performs significantly better than other classical adaptive resonance theory mapping models on the area under the receiver operating characteristic curves (AUC) and others criteria. Our algorithm gives an AUC of 86.42 %, 87.04 %, 91.08 %, and 89.24 % for the young female, middle aged female, young male, and middle-aged male subcategories, respectively. The proposed model can be used to support medical practitioners in accurate and early diagnosis of MetS.
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Recurrent kernel extreme reservoir machine for time series prediction
(2018-04-04)
Liu, Zongying
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Loo, Chu Kiong
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Masuyama, Naoki
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Pasupa, Kitsuchart
This paper proposes a novel recurrent multi-step-ahead prediction model called recurrent kernel extreme reservoir machine (RKERM) with quantum particle swarm optimization (QPSO). This model combines the strengths of recurrent kernel extreme learning machine (RKELM) and modified reservoir computing to overcome the limitations of prediction horizon with increased prediction accuracy based on reservoir computing theory. Furthermore, QPSO is used to optimize the parameters of kernel method and leaking rate of reservoir computing in the RKERM. In the experiment, we apply two synthetic benchmark data sets and five real-world time series data sets, including Malaysia palm oil price, ozone concentration in Toronto, sunspots, Standard Poor's 500, and water level at Phra Chulachomklao Fort in Thailand to evaluate the echo state network, recurrent support vector regression, recurrent extreme learning machine, RKELM, and RKERM. The experimental results show that the RKERM with QPSO has superior abilities in the different predicting horizons than others.
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Multiple steps time series prediction by a novel Recurrent Kernel Extreme Learning Machine approach
(2017-07-01)
Liu, Zongying
;
Loo, Chu Kiong
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Masuyama, Naoki
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Pasupa, Kitsuchart
This paper proposes a novel recurrent multi-steps- prediction model called Recurrent Kernel Extreme Learning Machine (RKELM). This model combines the strengths of recurrent multi-steps-prediction and Extreme Learning Machine (ELM) to unleash the limitation of prediction horizon. The kernel matrix is applied to replace the hidden layer mapping of ELM in order to solve the lack of predicting deterministic and parameter dependency. In the experiment, we apply two synthetic benchmark datasets and two real-world time series datasets including Malaysia palm oil price, ozone concentration of Toronto to evaluate RKELM and compare its performance against Recurrent Support Vector Regression (RSVR) and Recurrent Extreme Learning Machine (RELM). The experimental results show that RKELM has superior abilities in the different predicting horizons and stronger predicting deterministic than others.

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