The Jordan Pi-Sigma neural network for temperature prediction

In recent years, various temperature forecasting models have been proposed, which broadly can be classified into physically-based approaches and statistically-based approaches. Hitherto, those approaches involve sophisticated mathematical models to justify the use of empirical rules which make them less desirable for some applications. Therefore, in this respect, Neural Networks (NN) have been successfully applied and with no doubt, they provide the ability and potentials to predict the temperature events. However, the ordinary NN adopts computationally intensive training algorithms and can easily get trapped into local minima. To overcome such drawbacks in ordinary NN, this research focuses on using a Higher Order Neural Network (HONN). Pi-Sigma Neural Network (PSNN) which lies within this area, is able to maintain the high learning capabilities of HONN. The use of PSNN itself for temperature forecasting is preferably utilisable just yet. Notwithstanding, this study disposed towards an idea to develop a new network model called a Jordan Pi-Sigma Neural Network (JPSN) to overcome the drawbacks of ordinary NN, whilst taking the advantages of PSNN. JPSN, a network model with a single layer of tuneable weights with a recurrent term added in the network, is trained using the standard backpropagation gradient descent algorithm. The network was used to learn a set of historical temperature data of Batu Pahat region for five years (2005-2009), obtained from Malaysian Meteorological Department (MMD). JPSN’s ability to predict the future trends of temperature was tested and compared to that of Multilayer Perceptron (MLP) and the standard PSNN. Simulation results proved that JPSN’s forecast comparatively superior to MLP and PSNN models, with lower prediction error, thus revealing a great potential for JPSN as an alternative mechanism to both PSNN and ordinary NN in predicting the temperature measurement for one-step-ahead

Chemical and biological reactions of solidification of peat using ordinary portland cement (OPC) and coal ashes

Construction over peat area have often posed a challenge to geotechnical engineers. After decades of study on peat stabilisation techniques, there are still no absolute formulation or guideline that have been established to handle this issue. Some researchers have proposed solidification of peat but a few researchers have also discovered that solidified peat seemed to decrease its strength after a certain period of time. Therefore, understanding the chemical and biological reaction behind the peat solidification is vital to understand the limitation of this treatment technique. In this study, all three types of peat; fabric, hemic and sapric were mixed using Mixing 1 and Mixing 2 formulation which consisted of ordinary Portland cement, fly ash and bottom ash at various ratio. The mixtures of peat-binder-filler were subjected to the unconfined compressive strength (UCS) test, bacterial count test and chemical elemental analysis by using XRF, XRD, FTIR and EDS. Two pattern of strength over curing period were observed. Mixing 1 samples showed a steadily increase in strength over curing period until Day 56 while Mixing 2 showed a decrease in strength pattern at Day 28 and Day 56. Samples which increase in strength steadily have less bacterial count and enzymatic activity with increase quantity of crystallites. Samples with lower strength recorded increase in bacterial count and enzymatic activity with less crystallites. Analysis using XRD showed that pargasite (NaCa2[Mg4Al](Si6Al2)O22(OH)2) was formed in the higher strength samples while in the lower strength samples, pargasite was predicted to be converted into monosodium phosphate and Mg(OH)2 as bacterial consortium was re-activated. The Michaelis�Menten coefficient, Km of the bio-chemical reaction in solidified peat was calculated as 303.60. This showed that reaction which happened during solidification work was inefficient. The kinetics for crystallite formation with enzymatic effect is modelled as 135.42 (1/[S] + 0.44605) which means, when pargasite formed is lower, the amount of enzyme secretes is higher

Chaotic Time Series Forecasting Using Higher Order Neural Networks

This study presents a novel application and comparison of higher order neural networks (HONNs) to forecast benchmark chaotic time series. Two models of HONNs were implemented, namely functional link neural network (FLNN) and pi-sigma neural network (PSNN). These models were tested on two benchmark time series; the monthly smoothed sunspot numbers and the Mackey-Glass time-delay differential equation time series. The forecasting performance of the HONNs is compared against the performance of different models previously used in the literature such as fuzzy and neural networks models. Simulation results showed that FLNN and PSNN offer good performance compared to many previously used hybrid models

A Comprehensive Survey on Pi-Sigma Neural Network for Time Series Prediction

Prediction of time series grabs received much attention because of its effect on the vast range of real life applications. This paper presents a survey of time series applications using Higher Order Neural Network (HONN) model. The basic motivation behind using HONN is the ability to expand the input space, to solve complex problems it becomes more efficient and perform high learning abilities of the time series forecasting. Pi-Sigma Neural Network (PSNN) includes indirectly the capabilities of higher order networks using product cells as the output units and less number of weights. The goal of this research is to present the reader awareness about PSNN for time series prediction, to highlight some benefits and challenges using PSNN. Possible fields of PSNN applications in comparison with existing methods are presented and future directions are also explored in advantage with the properties of error feedback and recurrent networks

Predicting Physical Time Series Using Dynamic Ridge Polynomial Neural Networks

Forecasting naturally occurring phenomena is a common problem in many domains of science, and this has been addressed and investigated by many scientists. The importance of time series prediction stems from the fact that it has wide range of applications, including control systems, engineering processes, environmental systems and economics. From the knowledge of some aspects of the previous behaviour of the system, the aim of the prediction process is to determine or predict its future behaviour. In this paper, we consider a novel application of a higher order polynomial neural network architecture called Dynamic Ridge Polynomial Neural Network that combines the properties of higher order and recurrent neural networks for the prediction of physical time series. In this study, four types of signals have been used, which are; The Lorenz attractor, mean value of the AE index, sunspot number, and heat wave temperature. The simulation results showed good improvements in terms of the signal to noise ratio in comparison to a number of higher order and feedforward neural networks in comparison to the benchmarked techniques

Recurrent error-based ridge polynomial neural networks for time series forecasting

Time series forecasting has attracted much attention due to its impact on many practical applications. Neural networks (NNs) have been attracting widespread interest as a promising tool for time series forecasting. The majority of NNs employ only autoregressive (AR) inputs (i.e., lagged time series values) when forecasting time series. Moving-average (MA) inputs (i.e., errors) however have not adequately considered. The use of MA inputs, which can be done by feeding back forecasting errors as extra network inputs, alongside AR inputs help to produce more accurate forecasts. Among numerous existing NNs architectures, higher order neural networks (HONNs), which have a single layer of learnable weights, were considered in this research work as they have demonstrated an ability to deal with time series forecasting and have an simple architecture. Based on two HONNs models, namely the feedforward ridge polynomial neural network (RPNN) and the recurrent dynamic ridge polynomial neural network (DRPNN), two recurrent error-based models were proposed. These models were called the ridge polynomial neural network with error feedback (RPNN-EF) and the ridge polynomial neural network with error-output feedbacks (RPNN-EOF). Extensive simulations covering ten time series were performed. Besides RPNN and DRPNN, a pi-sigma neural network and a Jordan pi-sigma neural network were used for comparison. Simulation results showed that introducing error feedback to the models lead to significant forecasting performance improvements. Furthermore, it was found that the proposed models outperformed many state-of-the-art models. It was concluded that the proposed models have the capability to efficiently forecast time series and that practitioners could benefit from using these forecasting models

A Framework for Evaluating the Performance of Supply Chain Risk in E-commerce

The perceived risk is found to be a barrier for e-commerce application. It has been widely demonstrated in previous studies that the e-commerce is closely related with risk assessment. Taking into account of the scope of supply chain management, the activities of e-commerce system mostly deal with information flow, rather than either product or service flows. With regard to the rapid growth of e-commerce, there is imbalance between preparation and mitigation activities. More specifically, there is no formal model which shows supply chain risk in the e-commerce system, regarded as the research gap. Hence, one way to analyze and map out complex system as potential risk is to make Supply Chain Risk Management (SCRM) framework. This study is conducted to develop a framework about SCRM in the e-commerce area. Taking a case study on e-commerce based company, the SCRM framework is developed incorporating 8 perceived risk model in e-commerce: such as financial, social, time, performance, physical, privacy, security, and psychological risk. The expected contribution in theory and practice is discussed

IEOM Society International

IEOM Society Internationa

Proceedings of the 2nd Energy Security and Chemical Engineering Congress

Mechanical engineering is a field that is continuously evolving as a profession to provide sustainable design, products and technologies for society. Mechanical engineering products, in conjunction with technological advances in other sectors, contribute to noise, water and air pollution, and the degradation of land and landscape. The rate of production, both energy and products, is increasing at such a rapid rate that natural regeneration can no longer sustain. Emission control is a fast-growing topic for mechanical engineers and others, encompassing the development of machines and processes that produce fewer pollutants as well as new materials and processes that can decrease or eliminate pollution that has already been generated. And, in an increasingly environmentally conscious world, the concept of sustainability is also intrinsically important to the success or failure of any engineering product or processes. Mechanical engineers thus play a central role in applying a truly modern approach for enabling the global transition to green energy and sustainable prac-tices. To address climate change, researchers are progressively looking into a wide range of novel solutions for energy conversion, engine efficiency, alternative fuels, nature-inspired materials, enhanced manufacturing processes and so on. In this context, this book presents part of the proceedings of the Mechanical and Materials track of the 2nd Energy Security and Chemical Engineering Congress (ESChE 2021) as presented by the academics, researchers and postgraduate students. The book provides insights into different aspects of mechanical processes, nanoma-terials and alternate fuels that set the stage for development of sustainable techno-logical solutions. The content of this book will be useful for students, researchers and professionals working in the areas of mechanical engineering, materials, energy technologies, optimization and allied fields