Economic Lot-Size Using Machine Learning, Parallelism, Metaheuristic and Simulation

Sousa, Wilson Trigueiro de; Montevechi, J. A. B.; Miranda, Rafael de Carvalho; Rocha, F.; Vilela, Flávio Fraga

doi:10.2507/ijsimm18(2)461

Cited by 18 publications

(9 citation statements)

References 27 publications

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“…The identification and analysis of vehicle noise and vibration signals are shown in Figure 1, which is divided into two steps [12]:…”

Section: Correlation Algorithmmentioning

confidence: 99%

Recognition and Detection of Vehicle Noise and Vibration Signals Relying on Variable Step Size LMS Algorithm

Wang

2022

Mobile Information Systems

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Vehicle interior noise is a harmful sound formed in the vehicle interior under the influence of engine noise, transmission system noise, and body radiation noise during vehicle operation. The intensity of noise in the vehicle increases with the increase of vehicle speed and engine speed, which worsens the operating environment of the vehicle and does harm to the mood, spirit, and physiology of drivers and passengers. Because the noise is difficult to identify in the process of vehicle vibration signal recognition, aiming at the problem of vehicle vibration signal detection under the background of strong noise, combined with the characteristics of vehicle vibration signal, the variable step size LMS algorithm is applied to vehicle noise and vibration signal recognition. In this paper, based on the signal eigenvalues obtained from the vibration signal characteristics in the noise and vibration signal, the eigenvalues of the vehicle noise can be extended from the frequency domain to the complex plane through the operation of the variable step size LMS, and the separation law corresponding to the extended vibration intensity can be used at the same time. It is used to obtain data by the signal variable step size LMS algorithm, and the obtained optimal separability feature is used as the characteristic parameter of the vibration signal. Finally, the results of an example analysis show that the algorithm proposed in this paper can be used to identify different types of vehicle noise and vibration signals, has a certain anti-interference performance against noise, and can improve the recognition rate of vehicle noise and vibration signals.

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“…The identification and analysis of vehicle noise and vibration signals are shown in Figure 1, which is divided into two steps [12]:…”

Section: Correlation Algorithmmentioning

confidence: 99%

Recognition and Detection of Vehicle Noise and Vibration Signals Relying on Variable Step Size LMS Algorithm

Wang

2022

Mobile Information Systems

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“…2 shows the multivariate CNN-LSTM parameter settings for this experiment. It can deduce that the basic model is designed as follows based on the CNN-LSTM network's parameter settings: the input training set data is a threedimensional data vector (None,4,4), where the first number 4 represents the time step size and the other number 4 is the input dimensions of four properties, which in this case are four stock market indices of Shanghai, Japan, Singapore, and Indonesia.…”

Section: Model Evaluationmentioning

confidence: 99%

“…At the macroeconomic level, the movement of the stock market index, which is a financial time-series statistic, is often associated as one of the key indicators in determining a country's economic situation, making it a crucial issue to be examined over time [2]. e stock market index's movement is determined by a variety of internal and external influences, including the domestic and foreign economic climate, the international situation, industrial prospects, and stock market operations, but it is mostly influenced by the stock market index's historical meaning [3,4].…”

Section: Introductionmentioning

confidence: 99%

Multivariate CNN‐LSTM Model for Multiple Parallel Financial Time‐Series Prediction

2021

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At the macroeconomic level, the movement of the stock market index, which is determined by the moves of other stock market indices around the world or in that region, is one of the primary factors in assessing the global economic and financial situation, making it a critical topic to monitor over time. As a result, the potential to reliably forecast the future value of stock market indices by taking trade relationships into account is critical. The aim of the research is to create a time-series data forecasting model that incorporates the best features of many time-series data analysis models. The hybrid ensemble model built in this study is made up of two main components, each with its own set of functions derived from the CNN and LSTM models. For multiple parallel financial time-series estimation, the proposed model is called multivariate CNN-LSTM. The effectiveness of the evolved ensemble model during the COVID-19 pandemic was tested using regular stock market indices from four Asian stock markets: Shanghai, Japan, Singapore, and Indonesia. In contrast to CNN and LSTM, the experimental results show that multivariate CNN-LSTM has the highest statistical accuracy and reliability (smallest RMSE value). This finding supports the use of multivariate CNN-LSTM to forecast the value of different stock market indices and that it is a viable choice for research involving the development of models for the study of financial time-series prediction.

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“…With the boom of deep learning (DL) [5,17], many researchers have attempted to apply the DL in intelligent computing. The multilayered structure of deep neural networks (DNNs) can fit complex nonlinear mappings, and effectively prevent vanishing gradient [13,16].…”

Section: Introductionmentioning

confidence: 99%

A Prediction Model for Ultra-Short-Term Output Power of Wind Farms Based on Deep Learning

Wang¹,

Gao²,

Xu³

et al. 2020

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

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The output power prediction of wind farm is the key to effective utilization of wind energy and reduction of wind curtailment. However, the prediction of output power has long been a difficulty faced by both academia and the wind power industry, due to the high stochasticity of wind energy. This paper attempts to improve the ultra-short-term prediction accuracy of output power in wind farm. For this purpose, an output power prediction model was constructed for wind farm based on the time sliding window (TSW) and long short-term memory (LSTM) network. Firstly, the wind power data from multiple sources were fused, and cleaned through operations like dimension reduction and standardization. Then, the cyclic features of the actual output powers were extracted, and used to construct the input dataset by the TSW algorithm. On this basis, the TSW-LSTM prediction model was established to predict the output power of wind farm in ultra-short-term. Next, two regression evaluation metrics were designed to evaluate the prediction accuracy. Finally, the proposed TSW-LSTM model was compared with four other models through experiments on the dataset from an actual wind farm. Our model achieved a super-high prediction accuracy 92.7% as measured by d_MAE, an evidence of its effectiveness. To sum up, this research simplifies the complex prediction features, unifies the evaluation metrics, and provides an accurate prediction model for output power of wind farm with strong generalization ability.

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Economic Lot-Size Using Machine Learning, Parallelism, Metaheuristic and Simulation

Cited by 18 publications

References 27 publications

Recognition and Detection of Vehicle Noise and Vibration Signals Relying on Variable Step Size LMS Algorithm

Recognition and Detection of Vehicle Noise and Vibration Signals Relying on Variable Step Size LMS Algorithm

Multivariate CNN‐LSTM Model for Multiple Parallel Financial Time‐Series Prediction

A Prediction Model for Ultra-Short-Term Output Power of Wind Farms Based on Deep Learning

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