scholarly journals Neural Network with Specialized Knowledge for Forecasting Intermittent Demand

2020 ◽  
Author(s):  
Alexandre Crepory Abbott de Oliveira ◽  
Jéssica Mendes Jorge ◽  
Andrea Cristina dos Santos ◽  
Geraldo Pereira Rocha Filho
Keyword(s):  
Neural Network ◽  
Demand Forecasting ◽  
Performance Measure ◽  
Specialized Knowledge ◽  
Intermittent Demand ◽  
Forecasting Methods ◽  
The Neural Network ◽  
Comparison Results ◽  
Hidden Layer ◽  
Demand Seasonality

Demand forecasting is an essential part of an efficient inventory control system. However, when the demand has an intermittent or lumpy behavior, forecasting it becomes a challenging task. Several methods have been developed to solve this issue, but nonetheless, they only consider the information about the occurrence of demand, failing to assess the drivers of the data behavior. With the current digitalization of the industry, more data is available and, therefore, the chances of finding a causal relationship between the available data and the demand increases. Considering that, this paper proposes a single-hidden layer neural network for forecasting irregularly spaced time series with attributes conveying information about the past demand, seasonality of the data and specialized knowledge about the process. The neural network proposed is compared with benchmark neural networks and traditional forecasting methods for intermittent demand using three different performance measures on actual demand data from an industry operating in the aircraft maintenance sector. Statistical analysis is conducted on comparison results to identify significant differences in the forecasting methods according to each performance measure.

Building Electronic Commerce Recommendation System Based on Ontology Learning and BP Neural Network

2013 ◽  
Vol 718-720 ◽  
pp. 1961-1966
Author(s):  
Hong Sheng Xu ◽  
Qing Tan
Keyword(s):  
Neural Network ◽  
Electronic Commerce ◽  
Bp Neural Network ◽  
Optimization Problem ◽  
Recommendation System ◽  
High Efficiency ◽  
Ontology Learning ◽  
The Neural Network ◽  
Network Process ◽  
Hidden Layer

Electronic commerce recommendation system can effectively retain user, prevent users from erosion, and improve e-commerce system sales. BP neural network using iterative operation, solving the weights of the neural network and close values to corresponding network process of learning and memory, to join the hidden layer nodes of the optimization problem of adjustable parameters increase. Ontology learning is the use of machine learning and statistical techniques, with automatic or semi-automatic way, from the existing data resources and obtaining desired body. The paper presents building electronic commerce recommendation system based on ontology learning and BP neural network. Experimental results show that the proposed algorithm has high efficiency.

scholarly journals Advanced thyroid carcinomas: neural network analysis of ultrasonographic characteristics

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Michael Cordes ◽  
Theresa Ida Götz ◽  
Elmar Wolfgang Lang ◽  
Stephan Coerper ◽  
Torsten Kuwert ◽  
...  
Keyword(s):  
Neural Network ◽  
Imaging Modality ◽  
Follicular Carcinoma ◽  
Predictive Values ◽  
Thyroid Carcinomas ◽  
The Neural Network ◽  
Group 2 ◽  
Line Imaging ◽  
Hidden Layer ◽  
Group 1

Abstract Background Ultrasound is the first-line imaging modality for detection and classification of thyroid nodules. Certain characteristics observable by ultrasound have recently been identified that may indicate malignancy. This retrospective cohort study was conducted to test the hypothesis that advanced thyroid carcinomas show distinctive clinical and sonographic characteristics. Using a neural network model as proof of concept, nine clinical/sonographic features served as input. Methods All 96 study enrollees had histologically confirmed thyroid carcinomas, categorized (n = 32, each) as follows: group 1, advanced carcinoma (ADV) marked by local invasion or distant metastasis; group 2, non-advanced papillary carcinoma (PTC); or group 3, non-advanced follicular carcinoma (FTC). Preoperative ultrasound profiles were obtained via standardized protocols. The neural network had nine input neurons and one hidden layer. Results Mean age and the number of male patients in group 1 were significantly higher compared with groups 2 (p = 0.005) or 3 (p <  0.001). On ultrasound, tumors of larger volume and irregular shape were observed significantly more often in group 1 compared with groups 2 (p <  0.001) or 3 (p ≤ 0.01). Network accuracy in discriminating advanced vs. non-advanced tumors was 84.4% (95% confidence interval [CI]: 75.5–91), with positive and negative predictive values of 87.1% (95% CI: 70.2–96.4) and 92.3% (95% CI: 83.0–97.5), respectively. Conclusions Our study has shown some evidence that advanced thyroid tumors demonstrate distinctive clinical and sonographic characteristics. Further prospective investigations with larger numbers of patients and multicenter design should be carried out to show whether a neural network incorporating these features may be an asset, helping to classify malignancies of the thyroid gland.

Neuron Optimization Based PID Approach for Cutting Force Control

2006 ◽  
Vol 315-316 ◽  
pp. 85-89
Author(s):  
S. Jiang ◽  
Yan Shen Xu ◽  
J. Wu
Keyword(s):  
Neural Network ◽  
Controller Design ◽  
Open Loop ◽  
Network Control ◽  
Neural Network Control ◽  
System A ◽  
The Neural Network ◽  
Nc Milling ◽  
Hidden Layer ◽  
Control Principle

To improve the cutting efficiency, one of key approaches is to control with constant force in the full depth working condition. And the controller design is vital to realize the real-time feasibility and robustness of the system. A neuron optimization based PID approach is proposed in this paper and adopted in the NC cutting process. This approach optimizes the parameters of PID controller real-timely with the neural network control principle. It not only overcomes the mismatch of the open-loop system model which occurred in constant PID control, but also solves the contradiction between the calculation speed and precision in the neural network which caused by the node choosing of the hidden layer. At last, the simulation has been carried out on a NC milling machine to prove the validity and effectiveness of the proposed approach.

scholarly journals Intermittent demand forecasting for medical consumables with short life cycle using a dynamic neural network during the COVID-19 epidemic

2020 ◽  
Vol 26 (4) ◽  
pp. 3106-3122
Author(s):  
Peipei Liu
Keyword(s):  
Neural Network ◽  
Life Cycle ◽  
Model Selection ◽  
Demand Forecasting ◽  
Seasonal Adjustment ◽  
Intermittent Demand ◽  
Short Life ◽  
Dynamic Neural Network ◽  
Demand Models ◽  
The Impact

Accurate demand forecasting is always critical to supply chain management. However, many uncertain factors in the market make this issue a huge challenge. Especially during the current COVID-19 outbreak, the shortage of certain types of medical consumables has become a global problem. The intermittent demand forecast of medical consumables with a short life cycle brings some new challenges, such as the demand occurring randomly in many time periods with zero demand. In this research, a seasonal adjustment method is introduced to deal with seasonal influences, and a dynamic neural network model with optimized model selection procedure and an appropriate model selection criterion are introduced as the main forecasting models. In addition, in order to reduce the impact of zero demand, it adds some input nodes to the neural network by preprocessing the original input data. Lastly, a modified error measurement method is proposed for performance evaluation. Experimental results show that the proposed forecasting framework is superior to other intermittent demand models.

scholarly journals Prediction of Slag Characteristics Based on Artificial Neural Network for Molten Gasification of Hazardous Wastes

Energies ◽  
2020 ◽  
Vol 13 (19) ◽  
pp. 5115
Author(s):  
Xiongchao Lin ◽  
Wenshuai Xi ◽  
Jinze Dai ◽  
Caihong Wang ◽  
Yonggang Wang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Layer Structure ◽  
Hazardous Materials ◽  
Hazardous Wastes ◽  
Slag Viscosity ◽  
The Neural Network ◽  
Independent Test ◽  
Artificial Neural ◽  
Hidden Layer

Molten gasification is considered as a promising technology for the processing and safe disposal of hazardous wastes. During this process, the organic components are completely converted while the hazardous materials are safely embedded in slag via the fusion-solidification-vitrification transformation. Ideally, the slag should be glassy with low viscosity to ensure the effective immobilization and steady discharge of hazardous materials. However, it is very difficult to predict the characteristics of slag using existing empirical equations or conventional mathematical methods, due to the complex non-linear relationship among the phase transformation, vitrification transition and chemical composition of slag. Equipped with a strong nonlinear mapping ability, an artificial neural network may be able to predict the properties of slags if a large amount of data is available for training. In this work, over 10,000 experimental data points were used to train and develop a slag classification model (glassy vs. non-glassy) based on a neural network. The optimal structure of the neural network was figured out and validated. The results suggest that the classification accuracy for the independent test samples reached 93.3%. Using 1 and 0 as model inputs to represent mildly reducing and inert atmospheres, a double hidden layer structure in the neural network enabled the accurate classification of slags under various atmospheres. Furthermore, the neural network for the prediction of glassy slag viscosity was optimized; it featured a double hidden layer structure. Under a mildly reducing atmosphere, the absolute error from the independent test data was generally within 4 Pa·s. By adding a gas atmosphere into the input of the neural network using a simple normalization method, a multi-atmosphere slag viscosity prediction model was developed. Said model is much more accurate than its counterpart that does not consider the effect of the atmosphere. In summary, the artificial neural network proved to be an effective approach to predicting the slag properties under different atmospheres. The data-driven models developed in this work are expected to facilitate the commercial deployment of molten gasification technology.

A New Type of Pulse Neural Network Based on FPGA

2014 ◽  
Vol 556-562 ◽  
pp. 6081-6084
Author(s):  
Qian Huang ◽  
Wen Long Li ◽  
Jian Kang ◽  
Jun Yang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Sigmoid Function ◽  
Nonlinear Excitation ◽  
The Neural Network ◽  
Xor Problem ◽  
Quartus Ii ◽  
New Type ◽  
Hidden Layer ◽  
Eda Tools

In this paper, based on the study analyzed on the basis of a variety of neural networks, a kind of new type pulse neural network is implemented based on the FPGA [1]. The neural network adopts the Sigmoid function as its hidden layer nonlinear excitation function, at the same time, to reduce ROM table storage space and improve the efficiency of look-up table [2], it also adopts the STAM algorithm based nonlinear storage. Choose Altera Corporation’s EDA tools Quartus II as compilation, simulation platform, Cyclone II series EP2C20F484C6 devices and realized the pulse neural networks finally. In the last, we use XOR problem as example to carry out the hardware simulation, and simulation results are consistent with the theoretical value. Neural network to improve the complex, nonlinear, time-varying, uncertainty about the system reliability and security provides a new way.

scholarly journals Using neural networks to describe tracer correlations

2004 ◽  
Vol 4 (1) ◽  
pp. 143-146 ◽  
Author(s):  
D. J. Lary ◽  
M. D. Müller ◽  
H. Y. Mussa
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Chemical Models ◽  
The Neural Network ◽  
Pressure Time ◽  
Fortran Code ◽  
Time Of Year ◽  
Hidden Layer ◽  
And Training

Abstract. Neural networks are ideally suited to describe the spatial and temporal dependence of tracer-tracer correlations. The neural network performs well even in regions where the correlations are less compact and normally a family of correlation curves would be required. For example, the CH4-N2O correlation can be well described using a neural network trained with the latitude, pressure, time of year, and CH4 volume mixing ratio (v.m.r.). In this study a neural network using Quickprop learning and one hidden layer with eight nodes was able to reproduce the CH4-N2O correlation with a correlation coefficient between simulated and training values of 0.9995. Such an accurate representation of tracer-tracer correlations allows more use to be made of long-term datasets to constrain chemical models. Such as the dataset from the Halogen Occultation Experiment (HALOE) which has continuously observed CH4  (but not N2O) from 1991 till the present. The neural network Fortran code used is available for download.

scholarly journals Application of Firefly Algorithm based Neural Network Controller

2019 ◽  
Vol 8 (11) ◽  
pp. 2659-2662
Keyword(s):  
Neural Network ◽  
Pid Controller ◽  
Firefly Algorithm ◽  
Frequency Control ◽  
Optimization Technique ◽  
Load Frequency Control ◽  
Neural Network Controller ◽  
The Neural Network ◽  
Network Controller ◽  
Hidden Layer

The Firefly Algorithm is comparison of new optimize procedure based on PSO as tautness. The paper presents the competence and forcefulness of the Firefly algorithm as the optimize concept for a proportional–integral–derivative organizer under various loading conditions. The proposed PID controller is attempt to designed and implemented to frequency-control of a two area interconnected systems. The hidden layer formation is not personalized, as the interest lies only on the reckoning of the weights of the system. In sequence to obtain a practicable report, the weights of the neural network are computational or optimized by minimizing function cost or error. A Firefly Algorithm is an efficient but uncomplicated meta-heuristic optimization technique inspired by expected motion of fireflies towards more light, is used for the preparation of neural network. The simulation report view that the calculation competence of training progression using Firefly Optimization performance with Load frequency control. A study of the output report of the system PID controller and FA based neural network controllers are made for 1% change in load in area 1 and it is found that the proposed controllers ensures a better steady state response of the systems

scholarly journals Comparison of Feedforward and Recurrent Neural Network in Forecasting Chaotic Dynamical System

2019 ◽  
Vol 10 (37) ◽  
pp. 31-44
Author(s):  
Engin Kandıran ◽  
Avadis Hacınlıyan
Keyword(s):  
Neural Network ◽  
Dynamical System ◽  
Time Series ◽  
Function Approximation ◽  
Feedforward Neural Network ◽  
Network Architectures ◽  
Chaotic Dynamical System ◽  
Global Function ◽  
The Neural Network ◽  
Hidden Layer

Artificial neural networks are commonly accepted as a very successful tool for global function approximation. Because of this reason, they are considered as a good approach to forecasting chaotic time series in many studies. For a given time series, the Lyapunov exponent is a good parameter to characterize the series as chaotic or not. In this study, we use three different neural network architectures to test capabilities of the neural network in forecasting time series generated from different dynamical systems. In addition to forecasting time series, using the feedforward neural network with single hidden layer, Lyapunov exponents of the studied systems are forecasted.

scholarly journals Forecasting intermittent and sparse time series: A unified probabilistic framework via deep renewal processes

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0259764
Author(s):  
Ali Caner Türkmen ◽  
Tim Januschowski ◽  
Yuyang Wang ◽  
Ali Taylan Cemgil
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Discrete Time ◽  
Predictive Accuracy ◽  
Demand Forecasting ◽  
Trivial Extension ◽  
Renewal Processes ◽  
Probabilistic Forecasting ◽  
Unified Framework ◽  
Intermittent Demand

Intermittency are a common and challenging problem in demand forecasting. We introduce a new, unified framework for building probabilistic forecasting models for intermittent demand time series, which incorporates and allows to generalize existing methods in several directions. Our framework is based on extensions of well-established model-based methods to discrete-time renewal processes, which can parsimoniously account for patterns such as aging, clustering and quasi-periodicity in demand arrivals. The connection to discrete-time renewal processes allows not only for a principled extension of Croston-type models, but additionally for a natural inclusion of neural network based models—by replacing exponential smoothing with a recurrent neural network. We also demonstrate that modeling continuous-time demand arrivals, i.e., with a temporal point process, is possible via a trivial extension of our framework. This leads to more flexible modeling in scenarios where data of individual purchase orders are directly available with granular timestamps. Complementing this theoretical advancement, we demonstrate the efficacy of our framework for forecasting practice via an extensive empirical study on standard intermittent demand data sets, in which we report predictive accuracy in a variety of scenarios.

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