Learning Fuzzy Network Using Sequence Bound Global Particle Swarm Optimizer

2012 ◽  
Vol 2 (1) ◽  
pp. 54-70 ◽  
Author(s):  
Satchidananda Dehuri ◽  
Sung-Bae Cho
Keyword(s):  
Radial Basis Function Network ◽  
Experimental Studies ◽  
Particle Swarm ◽  
The Novel ◽  
Multi Layer Perceptron ◽  
Particle Swarm Optimizer ◽  
Output Layer ◽  
Hidden Layer ◽  
Original Domain ◽  
Fuzzy Network

This paper proposes an algorithm for classification by learning fuzzy network with a sequence bound global particle swarm optimizer. The aim of this work can be achieved in two folded. Fold one provides an explicit mapping of an input features from original domain to fuzzy domain with a multiple fuzzy sets and the second fold discusses the novel sequence bound global particle swarm optimizer for evolution of optimal set of connection weights between hidden layer and output layer of the fuzzy network. The novel sequence bound global particle swarm optimizer can solve the problem of premature convergence when learning the fuzzy network plagued with many local optimal solutions. Unlike multi-layer perceptron with many hidden layers it has only single hidden layer. The output layer of this network contains one neuron. This network advocates a simple and understandable architecture for classification. The experimental studies show that the classification accuracy of the proposed algorithm is promising and superior to other alternatives such as multi-layer perceptron and radial basis function network.

scholarly journals Natural Product Scores and Fingerprints Extracted from Artificial Neural Networks.

2021 ◽  
Author(s):  
Janosch Menke ◽  
Joana Massa ◽  
Oliver Koch
Keyword(s):  
Neural Networks ◽  
Natural Products ◽  
Artificial Neural Networks ◽  
Virtual Screening ◽  
Natural Product ◽  
Recent Work ◽  
The Novel ◽  
Multi Layer Perceptron ◽  
Output Layer ◽  
Depth Analysis

<div>Due to its desirable properties, natural products are an important ligand class for medicinal chemists. However, due to their structural distinctiveness, traditional cheminformatic approaches, like ligand-based virtual screening, often perform worse for natural products. Based on our recent work, we evaluated the ability of neural networks to generate fingerprints more appropriate for the use with natural products. A manually curated dataset of natural products and synthetic decoys was used to train a multi-layer perceptron network and an autoencoder-like network. An in-depth analysis showed that the extracted natural product specific neural fingerprints outperforms traditional as well as natural product specific fingerprints on three datasets. Further, we explore how the activation from the output layer of a network can work as a novel natural product likeness score. Overall two natural product specific datasets were generated, which are publicly available together with the code to create the fingerprints and the novel natural product likeness score.<br></div>

scholarly journals PENGENALAN WAJAH DUA DIMENSI MENGGUNAKAN MULTI-LAYER PERCEPTRON BERDASARKAN NILAI PCA DAN LDA

2013 ◽  
Vol 2 (1) ◽  
Author(s):  
Shofwatul Uyun ◽  
Muhammad Fadzlur Rahman
Keyword(s):  
Principal Component Analysis ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Multi Layer Perceptron ◽  
Linear Discriminant ◽  
Output Layer ◽  
Input Layer ◽  
Hidden Layer

Manusia memiliki kecerdasan multi intelligence yang sangat kompleks sehingga secara otomatis mampu mengenali seseorang yang pernah ditemui. Saat ini banyak sekali sistem pengenalan wajah yang sedang dikembangkan baik secara supervised maupun unsupervised. Jaringan Syaraf Tiruan (JST) merupakan salah satu metode supervised, dimana salah satu metode pembelajarannya disebut dengan Multi-Layer Perceptron (MLP). Penentuan banyaknya node pada hidden layer secara tepat mempengaruhi kinerja dari MLP pada sistem pengenalan wajah. Penelitian ini menggunakan 12 citra wajah sebagai data latih yang diekstraksi menjadi covarian matriks lalu diambil nilai eigen dari setiap data citra menggunakan metode principal component analysis (PCA) dan linear discriminant analysis (LDA). Setiap data menghasilkan 4 nilai eigen yang menjadi masukan pada algoritma pelatihan MLP yang menghasilkan nilai bobot optimal yang menjadi acuan untuk mengenali citra wajah. Berdasarkan hasil pengujian dan perbandingan variasi nilai parameter yang digunakan untuk mengenali citra wajah telah didapatkan nilai akurasi optimal sebesar 77,77%. Aristektur dari MLP tersebut terdiri atas : 4 node di input layer, 8 node di hidden layer dan 3 node di output layer dengan nilai epoch pelatihan sebesar 60x104.

scholarly journals Parallel and Cooperative Particle Swarm Optimizer for Multimodal Problems

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Geng Zhang ◽  
Yangmin Li
Keyword(s):  
Optimization Problems ◽  
Experimental Studies ◽  
Particle Swarm ◽  
Global Optimum ◽  
Orthogonal Experimental Design ◽  
Local Optimum ◽  
Particle Swarm Optimizer ◽  
Search Spaces ◽  
Function Vector ◽  
Multimodal Problems

Although the original particle swarm optimizer (PSO) method and its related variant methods show some effectiveness for solving optimization problems, it may easily get trapped into local optimum especially when solving complex multimodal problems. Aiming to solve this issue, this paper puts forward a novel method called parallel and cooperative particle swarm optimizer (PCPSO). In case that the interacting of the elements inD-dimensional function vectorX=[x1,x2,…,xd,…,xD]is independent, cooperative particle swarm optimizer (CPSO) is used. Based on this, the PCPSO is presented to solve real problems. Since the dimension cannot be split into several lower dimensional search spaces in real problems because of the interacting of the elements, PCPSO exploits the cooperation of two parallel CPSO algorithms by orthogonal experimental design (OED) learning. Firstly, the CPSO algorithm is used to generate two locally optimal vectors separately; then the OED is used to learn the merits of these two vectors and creates a better combination of them to generate further search. Experimental studies on a set of test functions show that PCPSO exhibits better robustness and converges much closer to the global optimum than several other peer algorithms.

scholarly journals Natural Product Scores and Fingerprints Extracted from Artificial Neural Networks.

2021 ◽  
Author(s):  
Janosch Menke ◽  
Joana Massa ◽  
Oliver Koch
Keyword(s):  
Neural Networks ◽  
Natural Products ◽  
Artificial Neural Networks ◽  
Virtual Screening ◽  
Natural Product ◽  
Recent Work ◽  
The Novel ◽  
Multi Layer Perceptron ◽  
Output Layer ◽  
Depth Analysis

<div>Due to its desirable properties, natural products are an important ligand class for medicinal chemists. However, due to their structural distinctiveness, traditional cheminformatic approaches, like ligand-based virtual screening, often perform worse for natural products. Based on our recent work, we evaluated the ability of neural networks to generate fingerprints more appropriate for the use with natural products. A manually curated dataset of natural products and synthetic decoys was used to train a multi-layer perceptron network and an autoencoder-like network. An in-depth analysis showed that the extracted natural product specific neural fingerprints outperforms traditional as well as natural product specific fingerprints on three datasets. Further, we explore how the activation from the output layer of a network can work as a novel natural product likeness score. Overall two natural product specific datasets were generated, which are publicly available together with the code to create the fingerprints and the novel natural product likeness score.<br></div>

scholarly journals Pengenalan Simbol Jarimatika Menggunakan Orientasi Histogram dan Multi-layer Perceptron

2015 ◽  
Vol 1 (4) ◽  
pp. 326
Author(s):  
Andi Sunyoto ◽  
Agus Harjoko
Keyword(s):  
Back Propagation ◽  
Optimal Number ◽  
Correct Classification ◽  
Multi Layer Perceptron ◽  
Output Layer ◽  
Input Layer ◽  
Static Images ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

Makalah ini membahas tentang pengenalan simbol-simbol Jarimatika menggunakan Jaringan Syaraf Tiruan (JST). Hasil penelitian ini dapat digunakan untuk pengembangan aplikasi perhitungan Jarimatika dan interaksi antara manusia dan komputer yang lebih natural. Segmentasi yang digunakan adalah orientasi histogram, algoritma JST yang digunakan adalah back propagation multi-layer perceptron. Layer-layer JST tersebut adalah satu layer input, satu hidden layer dan satu output layer. Penelitian ini betujuan untuk implementasi pengenalan pola simbol Jarimatika menggunakan JST multi-layer perceptron, implementasi harus mampu menghasilkan klasifikasi dengan benar, sistem harus mampu melakukan klasifikasi dari gambar statis, sehingga dapat menganalisa pengenalan gestur tangan dari simbol-simbol Jarimatika.Penelitian ini menggunakan 18 simbol dasar Jarimatika. Total citra yang digunakan adalah 360 yang terbagi atas 270 citra untuk training dan 90 citra untuk testing. Hasil penelitian ini menunjukkan bahwa JST multi-perceptron dapat digunakan untuk pengenalan simbol Jarimatika dengan akurasi 93.33%. Jumlah neuron yang optimal pada hidden layer adalah 725. Implementasi penelitian ini menggunakan Matlab versi 7 (R2010a).This paper focuses on the recognition of Jarimatika symbols using Artificial Neural Network (ANN). The results of this research can be used to develop applications for the Jarimatika and to make interaction between humans and computers more natural. The Segmentation used is orientation histograms, the ANN algorithm used is back propagation multi-layer perceptron. Th layers of the ANN are one input layer with 19 data, one hidden layer and one output layer. This research aims to implement Jarimatika symbols with pattern recognition and multi-layer perceptron algoritm, the implementation must be able to produce the correct classification, the system must be able to perform the classification of static images, so can analyze the recognition of hand gestures from Jarimatika symbols. This research uses 18 basic Jarimatika symbols. Total image used were 360, consisting of 270 images for training and 90 images for testing. The results of this study indicate that the multi-layer perceptron ANN can be used for recognition of Jarimatika symbols with accuracy 93.33%. The optimal number of neurons in the hidden layer is 725. Implementation of this research using Matlab version 7 (R2010a).

scholarly journals Adaptive Control Structure with Neural Data Processing Applied for Electrical Drive with Elastic Shaft

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3389
Author(s):  
Marcin Kamiński ◽  
Krzysztof Szabat
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Dynamic Properties ◽  
Experimental Studies ◽  
Drive System ◽  
Particle Swarm Optimizer ◽  
The Neural Network ◽  
Control Concept ◽  
Constant Coefficients ◽  
Selection Of

This paper presents issues related to the adaptive control of the drive system with an elastic clutch connecting the main motor and the load machine. Firstly, the problems and the main algorithms often implemented for the mentioned object are analyzed. Then, the control concept based on the RNN (recurrent neural network) for the drive system with the flexible coupling is thoroughly described. For this purpose, an adaptive model inspired by the Elman model is selected, which is related to internal feedback in the neural network. The indicated feature improves the processing of dynamic signals. During the design process, for the selection of constant coefficients of the controller, the PSO (particle swarm optimizer) is applied. Moreover, in order to obtain better dynamic properties and improve work in real conditions, one model based on the ADALINE (adaptive linear neuron) is introduced into the structure. Details of the algorithm used for the weights’ adaptation are presented (including stability analysis) to perform the shaft torque signal filtering. The effectiveness of the proposed approach is examined through simulation and experimental studies.

Quality inspection of complex-shaped metal parts by vibrations and an integrated Mahalanobis classification system

2020 ◽  
pp. 147592172097970
Author(s):  
Liangliang Cheng ◽  
Vahid Yaghoubi ◽  
Wim Van Paepegem ◽  
Mathias Kersemans
Keyword(s):  
Feature Selection ◽  
Mahalanobis Distance ◽  
Classification System ◽  
Gravitational Search Algorithm ◽  
Particle Swarm ◽  
Classification Performance ◽  
Decision Boundary ◽  
Particle Swarm Optimizer ◽  
Selection Phase

The Mahalanobis–Taguchi system is considered as a promising and powerful tool for handling binary classification cases. Though, the Mahalanobis–Taguchi system has several restrictions in screening useful features and determining the decision boundary in an optimal manner. In this article, an integrated Mahalanobis classification system is proposed which builds on the concept of Mahalanobis distance and its space. The integrated Mahalanobis classification system integrates the decision boundary searching process, based on particle swarm optimizer, directly into the feature selection phase for constructing the Mahalanobis distance space. This integration (a) avoids the need for user-dependent input parameters and (b) improves the classification performance. For the feature selection phase, both the use of binary particle swarm optimizer and binary gravitational search algorithm is investigated. To deal with possible overfitting problems in case of sparse data sets, k-fold cross-validation is considered. The integrated Mahalanobis classification system procedure is benchmarked with the classical Mahalanobis–Taguchi system as well as the recently proposed two-stage Mahalanobis classification system in terms of classification performance. Results are presented on both an experimental case study of complex-shaped metallic turbine blades with various damage types and a synthetic case study of cylindrical dogbone samples with creep and microstructural damage. The results indicate that the proposed integrated Mahalanobis classification system shows good and robust classification performance.

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