The undeterministic manipulation of solid models for robot program synthesis

Ali, Adel L.; Ali, Dia L.; Ali, Kamal S.

doi:10.1016/0360-8352(90)90160-n

“…In this context, manufacturing processes have intensely changed due to implementation of advanced manufacturing technologies specifically automation that can be done using computer programming and algorithms employing artificial intelligence (AI). In industrial AI, the process known as "training", enables the ma-quality control, and so on; However, there are various pros and cons as well such as Ali, et al (1990) explained that the number of variables that must be engaged into consideration while neural model formation has become excessively abundant [2]. Due to vagueness and sheer extent of items to be considered in calculation approaches cannot rationally resolve the problem; Therefore, ample development needs to be done in both the neural network complexity and computing capacity prior to real-intelligent program formation [3].…”

Section: Introductionmentioning

confidence: 99%

“…In industrial AI, the process known as "training", enables the ma-quality control, and so on; However, there are various pros and cons as well such as Ali, et al (1990) explained that the number of variables that must be engaged into consideration while neural model formation has become excessively abundant [2]. Due to vagueness and sheer extent of items to be considered in calculation approaches cannot rationally resolve the problem; Therefore, ample development needs to be done in both the neural network complexity and computing capacity prior to real-intelligent program formation [3]. These expert systems are found to be least efficient and effective in modern manufacturing systems due to complex, varying, and highly productive manufacturing environment.…”

Section: Introductionmentioning

confidence: 99%

Untitled

2021

Int J Signal Process Anal

0

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Machine learning based artificial neural networking (ANN) has been acknowledged to be an authentic way to accomplish intricate pattern identification and regression analysis without any obvious necessity to paradigm and resolve the primary physical models. ANN has been introduced and adopted in various fields of life based-on their key advantages including learning and adapting ability, parallel distributed computation, robustness, and many more. This review articles discussesthe working principle, classes and structure of ANN. The potential applicable areas of ANN, the challenges and their solutions that have been identified through literature survey, are discussed and summarized. KeywordsMachine learning, Artificial neural networking (ANN), Potential applications of ANN, Challenges and solutions in ANN chine learning algorithms to detect anomalies correlations while performing test searching for patterns across the various data feeds, so facilitating the manufacturing process and making it easier for people to work [1].Although, there are tremendous applications of AI in various aspects of manufacturing systems but expert systems are the most commonly used application of AI. Expert systems are commonly used in designing, scheduling, process planning,

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“…In this context, manufacturing processes have intensely changed due to implementation of advanced manufacturing technologies specifically automation that can be done using computer programming and algorithms employing artificial intelligence (AI). In industrial AI, the process known as "training", enables the ma-quality control, and so on; However, there are various pros and cons as well such as Ali, et al (1990) explained that the number of variables that must be engaged into consideration while neural model formation has become excessively abundant [2]. Due to vagueness and sheer extent of items to be considered in calculation approaches cannot rationally resolve the problem; Therefore, ample development needs to be done in both the neural network complexity and computing capacity prior to real-intelligent program formation [3].…”

Section: Introductionmentioning

confidence: 99%

“…In industrial AI, the process known as "training", enables the ma-quality control, and so on; However, there are various pros and cons as well such as Ali, et al (1990) explained that the number of variables that must be engaged into consideration while neural model formation has become excessively abundant [2]. Due to vagueness and sheer extent of items to be considered in calculation approaches cannot rationally resolve the problem; Therefore, ample development needs to be done in both the neural network complexity and computing capacity prior to real-intelligent program formation [3]. These expert systems are found to be least efficient and effective in modern manufacturing systems due to complex, varying, and highly productive manufacturing environment.…”

Section: Introductionmentioning

confidence: 99%

The Potential Applications of Machine Learning-Based Artificial Neural Networking Algorithms: A Review

Channa¹,

Arif²

2021

Int J Signal Process Anal

0

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Machine learning based artificial neural networking (ANN) has been acknowledged to be an authentic way to accomplish intricate pattern identification and regression analysis without any obvious necessity to paradigm and resolve the primary physical models. ANN has been introduced and adopted in various fields of life based-on their key advantages including learning and adapting ability, parallel distributed computation, robustness, and many more. This review articles discussesthe working principle, classes and structure of ANN. The potential applicable areas of ANN, the challenges and their solutions that have been identified through literature survey, are discussed and summarized. KeywordsMachine learning, Artificial neural networking (ANN), Potential applications of ANN, Challenges and solutions in ANN chine learning algorithms to detect anomalies correlations while performing test searching for patterns across the various data feeds, so facilitating the manufacturing process and making it easier for people to work [1].Although, there are tremendous applications of AI in various aspects of manufacturing systems but expert systems are the most commonly used application of AI. Expert systems are commonly used in designing, scheduling, process planning,

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