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Wu J.-Z.,Soochow University of Taiwan | Hao X.-C.,Waseda University | Chien C.-F.,National Tsing Hua University | Gen M.,Fuzzy Logic Systems Institute
Journal of Intelligent Manufacturing | Year: 2012

To improve capital effectiveness in light of demand fluctuation, it is increasingly important for hightech companies to develop effective solutions for managing multiple resources involved in the production. To model and solve the simultaneous multiple resources scheduling problem in general, this study aims to develop a genetic algorithm (bvGA) incorporating with a novel bi-vector encoding method representing the chromosomes of operation sequence and seizing rules for resource assignment in tandem. The proposed model captured the crucial characteristics that the machines were dynamic configuration among multiple resources with limited availability and sequence-dependent setup times of machine configurations between operations would eventually affect performance of a scheduling plan. With the flexibility and computational intelligence that GA empowers, schedule planners can make advanced decisions on integrated machine configuration and job scheduling. According to a number of experiments with simulated data on the basis of a real semiconductor final testing facility, the proposed bvGA has shown practical viability in terms of solution quality as well as computation time. © Springer Science+Business Media, LLC 2011.


Fukushima K.,Fuzzy Logic Systems Institute
Neural Networks | Year: 2013

This paper proposes new learning rules suited for training multi-layered neural networks and applies them to the neocognitron. The neocognitron is a hierarchical multi-layered neural network capable of robust visual pattern recognition. It acquires the ability to recognize visual patterns through learning. For training intermediate layers of the hierarchical network of the neocognitron, we use a new learning rule named add-if-silent. By the use of the add-if-silent rule, the learning process becomes much simpler and more stable, and the computational cost for learning is largely reduced. Nevertheless, a high recognition rate can be kept without increasing the scale of the network. For the highest stage of the network, we use the method of interpolating-vector. We have previously reported that the recognition rate is greatly increased if this method is used during recognition. This paper proposes a new method of using it for both learning and recognition. Computer simulation demonstrates that the new neocognitron, which uses the add-if-silent and the interpolating-vector, produces a higher recognition rate for handwritten digits recognition with a smaller scale of the network than the neocognitron of previous versions. © 2013 Elsevier Ltd.


Fukushima K.,Fuzzy Logic Systems Institute
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2014

The neocognitron is a multi-layered convolutional network that can be trained to recognize visual patterns robustly. This paper discusses a new neocognitron, which uses the add-if-silent rule for training intermediate layers and the method of interpolating-vector for classifying patterns at the highest stage of the hierarchical network. By the add-if-silent rule, a new cell is generated when all postsynaptic cells are silent. The generated cell learns the activity of the presynaptic cells in one-shot, and its input connections will never be modified afterward. Thus the training process is very simple, and does not require time-consuming calculation such as the gradient descent process. This paper analyzes how the size of training set affects the performance of the neocognitron and show that the add-if-silent rule can produce feature-extracting cells efficiently even with a small number of training patterns. © Springer International Publishing Switzerland.


Fukushima K.,Fuzzy Logic Systems Institute
Neural Networks | Year: 2011

The neocognitron is a hierarchical multi-layered neural network capable of robust visual pattern recognition. It has been demonstrated that recent versions of the neocognitron exhibit excellent performance for recognizing handwritten digits. When characters are written on a noisy background, however, recognition rate was not always satisfactory. To find out the causes of vulnerability to noise, this paper analyzes the behavior of feature-extracting S-cells. It then proposes the use of subtractive inhibition to S-cells from V-cells, which calculate the average of input signals to the S-cells with a root-mean-square. Together with this, several modifications have also been applied to the neocognitron. Computer simulation shows that the new neocognitron is much more robust against background noise than the conventional ones. © 2011 Elsevier Ltd.


Fukushima K.,Fuzzy Logic Systems Institute
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2014

This paper proposes an improved add-if-silent rule, which is suited for training intermediate layers of a multi-layered convolutional network, such as a neocognitron. By the add-if-silent rule, a new cell is generated if all postsynaptic cells are silent. The generated cell learns the activity of the presynaptic cells in one-shot, and its input connections will never be modified afterward. To use this learning rule for a convolutional network, it is required to decide at which retinotopic location this rule is to be applied. In the conventional add-if-silent rule, we chose the location where the activity of presynaptic cells is the largest. In the proposed new learning rule, a negative feedback is introduced from postsynaptic cells to presynaptic cells, and a new cell is generated at the location where the presynaptic activity fails to be suppressed by the feedback. We apply this learning rule to a neocognitron for hand-written digit recognition, and demonstrate the decrease in the recognition error. © 2014 Springer International Publishing Switzerland.

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