Cellular Optimal Linear Associative Memories for Statistical Process Control: A Preliminary Study and Proposal
نویسندگان
چکیده
The continuous detection and correction of unnatural process behaviours, due to special causes of variations, is a basic task in manufacturing to maintain any process stable and predictable. For this purpose, updated tools for Statistical Process Control have been studied so far, like the use of Artificial Neural Networks for pattern recognition in control charts. In this paper a preliminary study for the employment of Cellular Neural Networks (CNNs) for statistical process control is presented. In particular, the properties of CNNs when behaving as Optimal Linear Associative Memories in pattern recognition are analysed, with the aim of exploiting memories to recognize one or more patterns corresponding to reference behaviours on control charts. Discussion and future research trends are highlighted.
منابع مشابه
Performance Control for Manufacturing Sustainability: a Cellular Neural Network-based Approach
The recent trends in optimisation of sustainability of production processes requires, amongst all the activities, a continuous detection and correction of process behaviours, monitoring those parameters critical to performance. Detection of special causes of variations is a basic task in manufacturing, that has to be performed continuously to maintain any process stable as well as predictable. ...
متن کاملGeometric associative memories applied to pattern restoration
Two main research areas in Pattern Recognition are pattern classification and pattern restoration. In the literature, many models have been developed to solve many of the problems related to these areas. Among these models, Associative Memories (AMs) can be highlighted. An AM can be seen as a one-layer Neural Network. Recently, a Geometric Algebra based AM model was developed for pattern classi...
متن کاملHeuristic Continuous-time Associative Memories
In this paper, a novel associative memory model will be proposed and applied to memory retrievals based on the conventional continuous time model. The conventional model presents memory capacity is very low and retrieval process easily converges to an equilibrium state which is very different from the stored patterns. Genetic Algorithms is well-known with the capability of global optimal search...
متن کاملA Design Method of Cellular Neural Networks for Associative Memories based on Linear Programming
A novel design method of cellular neural networks (CNNs) for associative memories is presented. The proposed method can realize as high recall probability as the method based on generalized eigenvalue minimization (GEVM), which is known as the most efficient CNN design method so far. On the other hand, since the proposed method is based on linear programming, its computation time is much shorte...
متن کاملStatistical Modelling of a Preliminary Process for Depolymerisation of Cassava Non-starch Carbohydrate Using Organic Acids and Salt
A preliminary study on statistical modelling of a process for depolymerisation of cassava non-starch carbohydrate using halide salt assisted phosphoric and pyruvic acids were accomplished. The effects of three independent variables namely; acid concentration, potassium iodide salt and duration were studied using the central composite rotatable design on hydrolysis of the cassava non-starch carb...
متن کامل