Skip header Section
Skip Bibliometrics Section
Principles of Neurocomputing for Science and EngineeringSeptember 2000
- Authors:
- Fredric M. Ham,
- Ivica Kostanic
Skip Abstract Section
Abstract
From the Publisher:
This exciting new text covers artificial neural networks,but more specifically,neurocomputing. Neurocomputing is concerned with processing information,which involves a learning process within an artificial neural network architecture. This neural architecture responds to inputs according to a defined learning rule and then the trained network can be used to perform certain tasks depending on the application. Neurocomputing can play an important role in solving certain problems such as pattern recognition,optimization,event classification,control and identification of nonlinear systems,and statistical analysis.
"Principles of Neurocomputing for Science and Engineering," unlike other neural networks texts,is written specifically for scientists and engineers who want to apply neural networks to solve complex problems. For each neurocomputing concept,a solid mathematical foundation is presented along with illustrative examples to accompany that particular architecture and associated training algorithm.
The book is primarily intended for graduate-level neural networks courses,but in some instances may be used at the undergraduate level. The book includes many detailed examples and an extensive set of end-of-chapter problems.
Cited By
- Sun J, Wei Z and Liu X (2023). GRU-based model-free adaptive control for industrial processes, Neural Computing and Applications, 35:24, (17701-17715), Online publication date: 1-Aug-2023.
- Boubaker H, Canarella G, Gupta R and Miller S (2023). A Hybrid ARFIMA Wavelet Artificial Neural Network Model for DJIA Index Forecasting, Computational Economics, 62:4, (1801-1843), Online publication date: 1-Dec-2023.
- Franks J (2022). Text Classification for Records Management, Journal on Computing and Cultural Heritage , 15:3, (1-19), Online publication date: 30-Sep-2022.
- de Moura J, Neto J and Rêgo P (2020). A Neuro-Fuzzy Model for Online Optimal Tuning of PID Controllers in Industrial System Applications to the Mining Sector, IEEE Transactions on Fuzzy Systems, 28:8, (1864-1877), Online publication date: 1-Aug-2020.
- Ren G, Cao Y, Wen S, Huang T and Zeng Z (2018). A modified Elman neural network with a new learning rate scheme, Neurocomputing, 286:C, (11-18), Online publication date: 19-Apr-2018.
- Alshejari A and Kodogiannis V (2017). An intelligent decision support system for the detection of meat spoilage using multispectral images, Neural Computing and Applications, 28:12, (3903-3920), Online publication date: 1-Dec-2017.
- Thanh N, Kung Y, Chen S and Chou H (2016). Digital hardware implementation of a radial basis function neural network, Computers and Electrical Engineering, 53:C, (106-121), Online publication date: 1-Jul-2016.
- Buşoniu L, Daniels A and Babuška R (2016). Online learning for optimistic planning, Engineering Applications of Artificial Intelligence, 55:C, (70-82), Online publication date: 1-Oct-2016.
- Xie X, Bian G, Hou Z, Feng Z and Hao J (2016). Preliminary study on Wilcoxon-norm-based robust extreme learning machine, Neurocomputing, 198:C, (20-26), Online publication date: 19-Jul-2016.
- Chen W and Liu W (2015). Water quality modeling in reservoirs using multivariate linear regression and two neural network models, Advances in Artificial Neural Systems, 2015, (6-6), Online publication date: 1-Jan-2015.
- Liu D, Wang D, Wu J, Wang Y, Wang L, Zou X, Chen Y and Chen X (2014). A risk assessment method based on RBF artificial neural network-cloud model for urban water hazard, Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology, 27:5, (2409-2416), Online publication date: 1-Sep-2014.
- Zangooei M and Jalili S (2013). Protein fold recognition with a two-layer method based on SVM-SA, WP-NN and C4.5 TLM-SNC, International Journal of Data Mining and Bioinformatics, 8:2, (203-223), Online publication date: 1-Jul-2013.
- Wu J Prediction of rainfall time series using modular RBF neural network model coupled with SSA and PLS Proceedings of the 4th Asian conference on Intelligent Information and Database Systems - Volume Part II, (509-518)
- Sun M Time-Varying neurocomputing Proceedings of the 8th international conference on Intelligent Computing Theories and Applications, (18-26)
- Sartakhti J, Zangooei M and Mozafari K (2012). Hepatitis disease diagnosis using a novel hybrid method based on support vector machine and simulated annealing (SVM-SA), Computer Methods and Programs in Biomedicine, 108:2, (570-579), Online publication date: 1-Nov-2012.
- Mathewos B, Carvalho M and Ham F Network traffic classification using a parallel neural network classifier architecture Proceedings of the Seventh Annual Workshop on Cyber Security and Information Intelligence Research, (1-1)
- Kodogiannis V, Amina M and Lygouras J (2018). Power load forecasting using extended normalised radial basis function networks, Journal of Computational Methods in Sciences and Engineering, 11:4, (243-255), Online publication date: 1-Dec-2011.
- Meng C and Wu J A novel nonlinear neural network ensemble model using K-PLSR for rainfall forecasting Proceedings of the 7th international conference on Intelligent Computing: bio-inspired computing and applications, (41-48)
- Riahi-Madvar H, Ayyoubzadeh S and Atani M (2011). Developing an expert system for predicting alluvial channel geometry using ANN, Expert Systems with Applications: An International Journal, 38:1, (215-222), Online publication date: 1-Jan-2011.
- Özçelik S and Hardalaç N (2011). The statistical measurements and neural network analysis of the effect of musical education to musical hearing and sensing, Expert Systems with Applications: An International Journal, 38:8, (9517-9521), Online publication date: 1-Aug-2011.
- Zhou J, Pang C, Lewis F and Zhong Z (2011). Dominant Feature Identification for Industrial Fault Detection and Isolation Applications, Expert Systems with Applications: An International Journal, 38:8, (10676-10684), Online publication date: 1-Aug-2011.
- Liao K and Huang C Estimation of groutability of permeation grouting with microfine cement grouts using RBFNN Proceedings of the 8th international conference on Advances in neural networks - Volume Part III, (475-484)
- Chang C, Wang H and Su S (2010). Copyright authentication for images with a full counter-propagation neural network, Expert Systems with Applications: An International Journal, 37:12, (7639-7647), Online publication date: 1-Dec-2010.
- Zhang Y, Ma W and Cai B (2018). From Zhang neural network to Newton iteration for matrix inversion, IEEE Transactions on Circuits and Systems Part I: Regular Papers, 56:7, (1405-1415), Online publication date: 1-Jul-2009.
- Kale S and Dudul S (2018). Intelligent noise removal from EMG signal using focused time-lagged recurrent neural network, Applied Computational Intelligence and Soft Computing, 2009, (1-9), Online publication date: 1-Jan-2009.
- Badjate S and Dudul S (2018). Novel FTLRNN with gamma memory for short-term and long-term predictions of chaotic time series, Applied Computational Intelligence and Soft Computing, 2009, (1-9), Online publication date: 1-Jan-2009.
- Chang C, Li C, Chang Y and Jeng M Automatic die inspection for post-sawing LED wafers Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics, (1615-1620)
- Tsai H (2009). Hybrid high order neural networks, Applied Soft Computing, 9:3, (874-881), Online publication date: 1-Jun-2009.
- Panagou E and Kodogiannis V (2009). Application of neural networks as a non-linear modelling technique in food mycology, Expert Systems with Applications: An International Journal, 36:1, (121-131), Online publication date: 1-Jan-2009.
- Yuan Q, Dong C and Wang Q (2009). An adaptive fusion algorithm based on ANFIS for radar/infrared system, Expert Systems with Applications: An International Journal, 36:1, (111-120), Online publication date: 1-Jan-2009.
- Chang C, Li C, Chang J and Jeng M (2009). An unsupervised neural network approach for automatic semiconductor wafer defect inspection, Expert Systems with Applications: An International Journal, 36:1, (950-958), Online publication date: 1-Jan-2009.
- Hardalaç F (2009). Classification of educational backgrounds of students using musical intelligence and perception with the help of genetic neural networks, Expert Systems with Applications: An International Journal, 36:3, (6708-6713), Online publication date: 1-Apr-2009.
- Hu X (2009). Applications of the general projection neural network in solving extended linear-quadratic programming problems with linear constraints, Neurocomputing, 72:4-6, (1131-1137), Online publication date: 1-Jan-2009.
- Badjate S and Dudul S (2009). Multi step ahead prediction of north and south hemisphere sun spots chaotic time series using focused time lagged recurrent neural network model, WSEAS Transactions on Information Science and Applications, 6:4, (684-693), Online publication date: 1-Apr-2009.
- Bao E, Yang Y, Chen H, Lu Y, Liu X and Li W A study and implementation of self-adaptive allocation algorithm for parallel program Proceedings of the 5th international conference on Soft computing as transdisciplinary science and technology, (583-588)
- Chiang K and Huang Y (2008). An intelligent navigator for seamless INS/GPS integrated land vehicle navigation applications, Applied Soft Computing, 8:1, (722-733), Online publication date: 1-Jan-2008.
- Gudur P and Dixit U (2008). A neural network-assisted finite element analysis of cold flat rolling, Engineering Applications of Artificial Intelligence, 21:1, (43-52), Online publication date: 1-Feb-2008.
- Swift D and Dagli C (2008). A study on the network traffic of Connexion by Boeing, Engineering Applications of Artificial Intelligence, 21:8, (1113-1129), Online publication date: 1-Dec-2008.
- Chavarnakul T and Enke D (2008). Intelligent technical analysis based equivolume charting for stock trading using neural networks, Expert Systems with Applications: An International Journal, 34:2, (1004-1017), Online publication date: 1-Feb-2008.
- Sulehria H and Zhang Y Hopfield neural networks Proceedings of the 6th Conference on 6th WSEAS Int. Conf. on Artificial Intelligence, Knowledge Engineering and Data Bases - Volume 6, (125-130)
- Danisman K, Dalkiran I and Celebi F Design of a high precision temperature measurement system Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III, (997-1004)
- Woo Y, Han S and Jang K Off-Line signature verification based on directional gradient spectrum and a fuzzy classifier Proceedings of the First Pacific Rim conference on Advances in Image and Video Technology, (1018-1029)
- Zheng W, Zou C and Zhao L (2018). An Improved Algorithm for Kernel Principal Component Analysis, Neural Processing Letters, 22:1, (49-56), Online publication date: 1-Aug-2005.
- Diaz-Gomez P and Hougen D Analysis and mathematical justification of a fitness function used in an intrusion detection system Proceedings of the 7th annual conference on Genetic and evolutionary computation, (1591-1592)
- Li X, Han F, Lok T, Lyu M and Huang G Improvements to the conventional layer-by-layer BP algorithm Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part II, (189-198)
Please enable JavaScript to view thecomments powered by Disqus.
Recommendations
Learning Transformations with Complex-Valued Neurocomputing
The ability of the 1-n-1 complex-valued neural network to learn 2D affine transformations has been applied to the estimation of optical flows and the generation of fractal images. The complex-valued neural network has the adaptability and the ...