Machine learning for simulation-based support of early collaborative design

1998 ◽  
Vol 12 (2) ◽  
pp. 123-139 ◽  
Author(s):  
NENAD IVEZIC ◽  
JAMES H. GARRETT
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Constraint Satisfaction ◽  
Collaborative Design ◽  
Constraint Satisfaction Problem ◽  
Neural Network Approach ◽  
Design Processes ◽  
Simulation Based ◽  
And Performance ◽  
Categorization Scheme

The research and development of a simulation-based decision support system (SB-DSS) capable of assisting early collaborative design processes is presented. The requirements for such a system are included. Existing collaborative DSSs are shown to lack the capability to manipulate complex simulation-based relationships. On the other hand, advances within the machine learning in design community are shown to have a potential for providing, but have not yet addressed, simulation-based support for collaborative design processes. The developed SB-DSS is described in terms of its four principal components. First, the behavior-evaluation (BE) model is used to both structure individual, domain-specific decision models and organize these models into a collaborative decision model. Second, a probabilistic framework for the BE model enables management of the uncertainty inherent in learning and using simulation-based knowledge. Significantly, this framework provides a constraint satisfaction environment in which simulation-based knowledge is used. Third, a statistical neural network approach is used to capture simulation-based knowledge and build the probabilistic behavior models based on this knowledge. Fourth, since a probability distribution theory does not exist for the nonlinear neural network approaches, Monte Carlo simulation is introduced as a method to sample the trained neural networks and approximate the likelihoods of design variable values. Consequently, constraint satisfaction problem-solving capability is obtained. In addition, a mapping of the SB-DSS architecture onto a collaborative design agent framework is provided. Experimental evaluation of a prototype SB-DSS system is summarized, and performance of the SB-DSS with respect to search and usability metrics is documented. Initial results in developing the simulation-based support for collaborative design are encouraging. Lastly, a categorization of the machine learning approach and a critique of the proposed categorization scheme is presented.

An artificial neural network approach to discrete-event simulation

1995 ◽  
Vol 9 (1) ◽  
pp. 37-49 ◽  
Author(s):  
Ian Flood ◽  
Kenneth Worley
Keyword(s):  
Neural Network ◽  
Discrete Event Simulation ◽  
Network Architecture ◽  
Discrete Event ◽  
Network Approach ◽  
Neural Network Approach ◽  
Event Simulation ◽  
The Neural Network ◽  
Component Processes ◽  
And Performance

AbstractThis paper proposes and evaluates a neural network-based method for simulating manufacturing processes that exhibit both noncontinuous and stochastic behavior processes more conventionally modeled, using discrete-event simulation algorithms. The incentive for developing the technique is its potential for rapid execution of a simulation through parallel processing, and facilitation of the development and improvement of models particularly where there is limited theory describing the dependence between component processes. A brief introduction is provided to a radial-Gaussian neural network architecture and training process, the system adopted for the work presented in this paper. A description of the basic approach proposed for applying this technology to simulation is then described. This involves the use of a modularized neural network approach to model construction and the prediction of the occurrence of events using information retained from several previous states of the simulation. A class of earth-moving systems, comprising a push-dozer and a fleet of scrapers, is used as the basis for assessing the viability and performance of the proposed approach. A series of experiments show the neural network to be capable of both capturing the characteristic behavior and making an accurate prediction of production rates of scraper-based earth-moving systems. The paper concludes with an indication of some areas for further development and evaluation of the technique.

scholarly journals A Novel Method for Intrusion Detection Based on SARSA and Radial Bias Feed Forward Network (RBFFN)

2013 ◽  
Vol 7 (3) ◽  
pp. 646-653
Author(s):  
Anshul Chaturvedi ◽  
Prof. Vineet Richharia
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Intrusion Detection ◽  
Rbf Neural Network ◽  
Neural Network Approach ◽  
Feed Forward Network ◽  
Machine Learning Approach ◽  
Network Process ◽  
Reinforced Learning ◽  
Attack Data

The Internet, computer networks and information are vital resources of current information trend and their protection has increased importance in current existence. Any attempt, successful or unsuccessful to finding the middle ground the discretion, truthfulness and accessibility of any information resource or the information itself is measured a security attack or an intrusion. Intrusion compromised a loose of information credential and trust of security concern. The mechanism of intrusion detection faced a problem of new generated schema and pattern of attack data. Various authors and researchers proposed a method for intrusion detection based on machine learning approach and neural network approach all these compromised with new pattern and schema. Now in this paper a new model of intrusion detection based on SARAS reinforced learning scheme and RBF neural network has proposed. SARAS method imposed a state of attack behaviour and RBF neural network process for training pattern for new schema. Our empirical result shows that the proposed model is better in compression of SARSA and other machine learning technique.

scholarly journals A Review on Machine Learning Models in Injection Molding Machines

2022 ◽  
Vol 2022 ◽  
pp. 1-28
Author(s):  
Senthil Kumaran Selvaraj ◽  
Aditya Raj ◽  
R. Rishikesh Mahadevan ◽  
Utkarsh Chadha ◽  
Velmurugan Paramasivam
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Injection Molding ◽  
Performance Criteria ◽  
Machine Learning Techniques ◽  
Injection Molding Process ◽  
Molding Process ◽  
Injection Speed ◽  
Detection Techniques ◽  
And Performance

One of the most suitable methods for the mass production of complicated shapes is injection molding due to its superior production rate and quality. The key to producing higher quality products in injection molding is proper injection speed, pressure, and mold design. Conventional methods relying on the operator’s expertise and defect detection techniques are ineffective in reducing defects. Hence, there is a need for more close control over these operating parameters using various machine learning techniques. Neural networks have considerable applications in the injection molding process consisting of optimization, prediction, identification, classification, controlling, modeling, and monitoring, particularly in manufacturing. In recent research, many critical issues in applying machine learning and neural network in injection molding in practical have been addressed. Some problems include data division, collection, and preprocessing steps, such as considering the inputs, networks, and outputs, algorithms used, models utilized for testing and training, and performance criteria set during validation and verification. This review briefly explains working on machine learning and artificial neural network and optimizing injection molding in industries.

A dyadic of employee readiness and job satisfaction

2018 ◽  
Vol 26 (5) ◽  
pp. 842-857 ◽  
Author(s):  
Brian Matthews ◽  
Jamie Daigle ◽  
Melissa Houston
Keyword(s):  
Neural Network ◽  
Job Satisfaction ◽  
Job Performance ◽  
Network Approach ◽  
Theoretical Frameworks ◽  
Dyadic Relationship ◽  
Neural Network Approach ◽  
Content Type ◽  
And Performance ◽  
Leadership Satisfaction

Purpose The purpose of this paper is to examine the linkages between leadership and satisfaction models with neural networks to epistemologically explore both the theoretical and practical basis of these paradigms to analyze the effect employee readiness has on job satisfaction. A review of the literature indicates an absence of a paradigmatic precursor to the satisfaction-performance dyadic. Revisiting theoretical frameworks builds a reconceptualized prism that amalgamates leadership and job satisfaction constituents to form a theoretical scaffold and linkage between employee readiness and job satisfaction. Design/methodology/approach Reviewing the literature explores a theoretical existence of a readiness model preceding the satisfaction-performance paradigm that measures how the amalgam of readiness variables affects job satisfaction. This conceived theory uses a unidirectional model that extends the linear progression and institutes a backwards propagation linkage to the satisfaction-performance linkage using the following unidirectional correlation: readiness-satisfaction→ satisfaction-performance. Using a neural network approach, a total of 160 companies are integrated into a simulation using leadership, satisfaction and readiness variables, with an emphasize on high relationship, to ascertain the effect of readiness on job satisfaction. Findings While there are studies that interchangeably link satisfaction and performance, revisiting the literature provides theoretical insight that validates the formation of a preceding construct that converges leadership and satisfaction constituencies to form a dyadic relationship between readiness and satisfaction. Research has tirelessly attempted to discover variable correlation between job performance and job satisfaction. However, these attempts are met with contradictory results. To truly link employee readiness to the job satisfaction/job performance dyad, a neural network is created, which deduces that random probabilities confirm the continuous exactitude of a positive correlation between readiness and job satisfaction. This, in turn, confirms an existent theoretical precursor to the satisfaction-performance paradigm. The implications of not linking job readiness to satisfaction and performance can potentially leave managers amiss when triangulating performance decline. Reclassifying the satisfaction-performance dyadic corroborates Judge et al.’s (2001) theory that reinventions of this impression should be researched, and Graen and Uhl-Bien’s (1991) conclusive remarks that an evaluation beyond “trait-like” individual differences of leaders is necessary to recognize the leadership paradigm loop, which is inclusive of the leader, the follower and the dyadic relationship. Originality/value This research paper is useful for practitioners and academics to refer as the comparative and intersecting explanation of leadership and job satisfaction models, as it peripherally conveys a legitimate view of a preceding relational construct that will add value to the relevance of employee readiness as it affects job satisfaction. In addition, the neural network approach is a sound and unique method to algorithmically validate the correlation between job satisfaction models and leadership. Through codifying, the environmental variables comprised Herzberg et al.’s (1959) motivation and hygiene factors that are directly related to a leader-member exchange function, an evidentiary linkage validates the literature works of Hersey and Blanchard (2001) and directly links it to job satisfaction precursors.

scholarly journals An Evaluation Study on Investment Efficiency: A Predictive Machine Learning Approach

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Weiwei Hao ◽  
Hongyan Gao ◽  
Zongqing Liu
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Structural Equation ◽  
Evaluation Index System ◽  
Corporate Debt ◽  
Investment Efficiency ◽  
Neural Network Approach ◽  
Ownership Reform ◽  
Nonlinear Autoregressive ◽  
Autoregressive Neural Network

This paper proposes a nonlinear autoregressive neural network (NARNET) method for the investment performance evaluation of state-owned enterprises (SOE). It is different from the traditional method based on machine learning, such as linear regression, structural equation, clustering, and principal component analysis; this paper uses a regression prediction method to analyze investment efficiency. In this paper, we firstly analyze the relationship between diversified ownership reform, corporate debt leverage, and the investment efficiency of state-owned enterprises (SOE). Secondly, a set of investment efficiency evaluation index system for SOE was constructed, and a nonlinear autoregressive neural network approach was used for verification. The data of A-share state-owned listed companies in Shanghai and Shenzhen stock exchanges from 2009 to 2018 are taken as a sample. The experimental results show that the output value from the NARNET is highly fitted to the actual data. Based on the neural network model regression analysis, this paper conducts a descriptive statistical analysis of the main variables and control variables of the evaluation indicators. It verifies the direct impact of diversified ownership reform on the investment efficiency of SOE and the indirect impact on the investment efficiency of SOE through corporate debt leverage.

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