Refine
Document Type
- Conference Object (11)
- Article (3)
- Report (2)
Keywords
- Agile production (1)
- Chaos (1)
- Decision‐support systems (1)
- Hybrid systems (1)
- Open systems (1)
A framework of decision‐support systems in advanced manufacturing enterprises ‐ a systems view
(1997)
Analyses the environment and characteristics of an advanced manufacturing system (AMS). It is an open system with a multi‐layer structure and a self‐organizing ability capable of responding to a continuous changing and unpredictable environment in this information age. Based on the analysis, summarizes the requirements of decision processes in a typical AMS, and presents a framework of a decision‐support system (DSS) in an advanced manufacturing enterprise. Outlines the conceptual modelling of the system, explains the work carried out by an inter‐disciplinary team composed of researchers from the 863/CIMS/I‐MADIS, a national hi‐tech R&D programme in China and a joint research programme in computer integrated manufacturing management between the City University of Hong Kong and Tsinghua University, Beijing. 863/CIMS is one of the subject themes under the auspices of automation technology of the National High Technology Research and Development Programme of China launched by the government in March 1986.
During planning and designing of new products the design managers are interested in estimating the cost as early as possible. However, at the early design stage only a few attributes of the future product are known, and their impact on cost is not clear to the cost estimation expert. Neural networks can be used to detect the hidden relationships between cost drivers and the cost of a new product, and estimate the cost after being presented a small set of conceptual attributes describing the product.
Usually, knowledge to be learned by neural networks is represented implicitly in the training samples. The ability to insert knowledge apart from the implicit representations in training samples (“background knowledge”) gives rise to the hope that the learning and operation behavior of neural networks can be improved. In this paper, we develop a method to accomplish the insertion of expert knowledge into the error function during training.
Describes the concept of neural engineering which supports decision-making in the systematic construction of neural networks. We gave some guidelines on building neural networks tailored to a specific applications, such as: what is the optimal number of hidden nodes, how to prune the input nodes, segregation and transformation of data etc. Experiments were carried on cost estimation using a multi-layer perceptron (backpropagation algorithm)
信息时代的制造业及信息的价值 任守
(1995)