Improving recognition and generalization capability of back-propagation NN using a self-organized network inspired by immune algorithm (SONIA)

Widyanto, M. Rahmat; Nobuhara, Hajime; Kawamoto, Kazuhiko; Hirota, Kaoru; Kusumoputro, Benyamin

doi:10.1016/j.asoc.2004.10.008

Cited by 49 publications

(29 citation statements)

References 11 publications

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“…As shown previously, Widyanto et al [70] introduce a method to improve the recognition as well as the generalization capabilities of the backpropagation algorithm. Our proposed neural network architecture extends the SONIA network by introducing recurrent links.…”

Section: Dynamic Self-organised Network Inspired By the Immune Algorimentioning

confidence: 99%

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Dynamic neural network architecture inspired by the immune algorithm to predict preterm deliveries in pregnant women

et al. 2015

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There has been some improvement in the treatment of preterm infants, which has helped to increase their chance of survival. However, the rate of premature births is still globally increasing. As a result, this group of infants are most at risk of developing severe medical conditions that can affect the respiratory, gastrointestinal, immune, central nervous, auditory and visual systems. There is a strong body of evidence emerging that suggests the analysis of uterine electrical signals, from the abdominal surface (Electrohysterography -EHG), could provide a viable way of diagnosing true labour and even predict preterm deliveries. This paper explores this idea further and presents a new dynamic self-organised network immune algorithm that classifies term and preterm records, using an open dataset containing 300 records (38 preterm and 262 term). Using the dataset, oversampling and cross validation techniques are evaluated against other similar studies. the proposed approach shows an improvement on existing studies with 89% sensitivity, 91% specificity, 90% positive predicted value, 90% negative predicted value, and an overall accuracy of 90%.

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Section: Dynamic Self-organised Network Inspired By the Immune Algorimentioning

confidence: 99%

“…This procedure will be repeated until all inputs have found their corresponding hidden unit as follows [70]:…”

Section: Self-organised Multilayer Network Inspired By Immune Algorithmmentioning

confidence: 99%

Dynamic neural network architecture inspired by the immune algorithm to predict preterm deliveries in pregnant women

et al. 2015

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“…The input layer, hidden layer, and output layer are connected by a forward mode, and the number of neural neurons in each layer may be different. The connection weights of neural neurons can be trained by the error BP algorithm between the input layer and the hidden layer as well as between the hidden layer and output layer, and the thresholds among the three layers can also be changed in this way [49]. BP networks generally use the steepest descent gradient method (SDGM) to revise the weights and thresholds [50].…”

Section: Bp-ann-based Learning/recognitionmentioning

confidence: 99%

A SA‐ANN‐Based Modeling Method for Human Cognition Mechanism and the PSACO Cognition Algorithm

Chen

Tan

2018

Complexity

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Artificial neural networks (ANNs) are the important approaches for researching human cognition process. However, current ANNsbased cognition methods cannot address the problems of complex information understanding and fault-tolerant learning. Here we present a modeling method for cognition mechanism based on a simulated annealing-artificial neural network (SA-ANN). Firstly, the relationship between SA processing procedure and cognition knowledge evolution is analyzed, and a SA-ANN-based inference model is set up. Then, based on the inference model, a Powell SA with combinatorial optimization (PSACO) algorithm is proposed to improve the clustering efficiency and recognition accuracy for the cognition process. Finally, three groups of numerical instances for knowledge clustering are provided, and three comparative experiments are performed by self-developed cognition software. The simulated results show that the proposed method can increase the convergence rate by more than 20%, compared with the back-propagation (BP), SA, and restricted Boltzmann machines based extreme learning machine (RBM-ELM) algorithms. The comparative cognition experiments prove that the method can obtain better performances of information understanding and fault-tolerant learning, and the cognition accuracies for original sample, damaged sample, and transformed sample can reach 99.6%, 99.2%, and 97.1%, respectively.

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“…The selection of these parameters is very important to improve the performance of neural networks. Furthermore, the MLP neural network is affected by some learning algorithm problems such as overfitting problems [10][11][12]. This means that the neural network can perfectly map between input and output in training data but it will not be able to sufficiently generalise its learning to new data.…”

Section: Introductionmentioning

confidence: 99%

“…There are a number of studies which have investigated the ability to use different techniques to improve the generalisation ability of feed-forward neural networks and to automatically select the best number of hidden units and their weights. One of these techniques was proposed by Widyanto et al [12]. They designed a self-organised hidden layer inspired by immune algorithm (SONIA).…”

Section: Introductionmentioning

confidence: 99%

The application of dynamic self-organised multilayer network inspired by the Immune Algorithm for weather signals forecast

Hussain

Al‐Jumeily

Alaskar

2015

2015 Third International Conference on Technological Advances in Electrical, Electronics and Computer Engineering (TAEECE)

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Improving recognition and generalization capability of back-propagation NN using a self-organized network inspired by immune algorithm (SONIA)

Cited by 49 publications

References 11 publications

Dynamic neural network architecture inspired by the immune algorithm to predict preterm deliveries in pregnant women

Dynamic neural network architecture inspired by the immune algorithm to predict preterm deliveries in pregnant women

A SA‐ANN‐Based Modeling Method for Human Cognition Mechanism and the PSACO Cognition Algorithm

The application of dynamic self-organised multilayer network inspired by the Immune Algorithm for weather signals forecast

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