Neural Networks in Manufacturing: Possible Impacts on Cutting Stock Problems

The potential of neural networks is examined, and the effect of parallel processing on the solution of the stock-cutting problem is assessed. The conceptual model proposed integrates a feature-recognition network and a simulated annealing approach. The model uses a neocognitron neural network paradigm to generate data for assessing the degree of match between two irregular patterns. The information generated through the feature recognition network is passed to an energy function, and the optimal configuration of patterns is computed using a simulated annealing algorithm. Basics of the approach are demonstrated with an example

Engineering Cyber Physical Systems: Machine Learning, Data Analytics and Smart Systems Architecting Preface

Multi-faceted systems of the future will entail complex logic with many levels of reasoning in intricate arrangement. The organization of these systems involves a web of connections and demonstrates self-driven adaptability. They are designed for autonomy and may exhibit emergent behavior that can be visualized. We are building systems that are created by a network of physical objects that contain embedded technology to communicate and interact with their internal states or the external environment. These changes in technology and deployment of system of systems having these new characteristics are demanding new ways of thinking and engineering. These are complex adaptive systems that can have emergent behavior and require systems integration and engineering in their design and operation

Engineering Cyber Physical Systems: Preface

Multi-faceted systems of the future will entail complex logic with many levels of reasoning in intricate arrangement. The organization of these systems involves a web of connections and demonstrates self-driven adaptability. They are designed for autonomy and may exhibit emergent behavior that can be visualized. Complex Adaptive Systems have dynamically changing meta-architectures. Finding an optimal architecture for these systems is a multi-criteria decision making problem often involving many objectives in the order of 20 or more. This creates Pareto Breakdown which prevents ordinary multi-objective optimization approaches from effectively searching for an optimal solution; saturating the decision maker with large sets of solutions that may not be representative for a compromise architecture selection from the solution space

Advantages of using Fuzzy Class Memberships in Self-Organizing Map and Support Vector Machines

The self-organizing map (SOM) is naturally unsupervised learning, but if a class label is known, it can be used as the classifier. In a SOM classifier, each neuron is assigned a class label based on the maximum class frequency and classified by a nearest neighbor strategy. The drawback when using this strategy is that each pattern is treated by equal importance in counting class frequency regardless of its typicalness. For this reason, the fuzzy class membership can be used instead of crisp class frequency and this fuzzy membership-label neuron provides another perspective of a feature map. This fuzzy class membership can be also used to select training samples in a support vector machine (SVM) classifier. This method allows us to reduce the training set as well as support vectors without significant loss of classification performance

An Object-Based Evolutionary Algorithm for Solving Rectangular Piece Nesting Problems

Nesting problems have been tackled by researchers using a vast number of algorithms in the past. Most of the algorithms, however, need to perform on a one-dimensional space. Therefore, the problem must be transformed into a one-dimensional space problem similar to the travelling salesman problem. Consequently, loss of solutions due to the dimensional reduction may occur. In this study, an object-based evolutionary algorithm for rectangular piece nesting problems is proposed. This methodology is created on truly two-dimensional space, allowing new mechanisms (i.e., individual representation, initialization, etc.) and new object-based genetic operators (i.e., hill-climbing, mutation, and recombination operators) to perform effectively on the space. Since no dimensional reduction is used, therefore, no solution losses during the searching. Simulation/animation of the layouts shows the continual improvement by using this method over generations. Experimental results are promising

Simple Ensemble-Averaging Model Based on Generalized Regression Neural Network in Financial Forecasting Problems

Introduces an ensemble-averaging model based on a GRNN (generalized regression neural network) for financial forecasting. The model trains all input individually using GRNNs and uses a simple ensemble-averaging committee machine to improve the accuracy performance. In a financial problem, there are many different factors that can effect the asset price movement at different times. An experiment is implemented in two different data sets, S&P 500 index and currency exchange rate. The predictive abilities of the model are evaluated on the basis of root mean squared error, standard deviation and percent direction correctness. The study shows a promising result of the model in both data sets

Web Personalization using Neuro-Fuzzy Clustering Algorithms

Different users have different needs from the same web page and hence it is necessary to develop a system which understands the needs and demands of the users. Web server logs have abundant information about the nature of users accessing it. In this paper we discussed how to mine these web server logs for a given period of time using unsupervised and competitive learning algorithm like Kohonen\u27\u27s self organizing maps (SOM) and interpreting those results using Unified distance Matrix (U-matrix). These algorithms help us in efficiently clustering users based on similar web access patterns and each cluster having users with similar browsing patterns. These clusters are useful in web personalization so that it communicates better with its users and also in web traffic analysis for predicting web traffic at a given period of time

Simulated Annealing Approach for Solving Stock Cutting Problem

The simulated annealing approach is applied to stock cutting. The conceptual approach proposed uses an energy function that measures the area of the rectangular enclosure of all the patterns to be nested, the level of similarity between pattern pairs, and the amount of overlap among patterns in evaluating various pattern configurations to be generated by the simulated annealing algorithm. Three methods for pattern configuration generation are considered. The first method uses heuristics to generate the initial configuration. The second and third methods use random selection and random placement of pattern