Adaptive pattern recognition and neural networks

By Y. Pao. 1989

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Computationally efficient FLANN-based intelligent stock price prediction system (2009, methodology): “Our system tries to overcome this problem and increase the prediction quality by using a computationally efficient and reliable functional link artificial neural network (FLANN), which was originally proposed by Pao [12].”
The Effects of Adding Noise During Backpropagation Training on a Generalization Performance (1996, background): “Geman and Hwang (1986) and Kushner (1987) proved the asymptotic convergence to the global minimum of E(w), provided that the temperature is reduced slowly.,Assuming the cost of misclassifying Class A is the same as that of misclassifying Class B, one can derive the optimal classification rule for ”
A PSO based Functional Link Artificial Neural Network training algorithm for equalization of digital communication channels (2010, result): “Performance comparison between FLANN based equalizer and an LMS equalizer has been reported [10].”
Day-ahead price forecasting of electricity markets by a new fuzzy neural network (2006, background): “7, recommended values for the GDR training mechanism [18], [19].”
Bayesian methods for neural networks (2000, background): “2) encompasses a large number of nonlinear estimation methods including projection pursuit regression (Friedman and Stuetzle, 1981; Huber, 1985), Volterra series (Billings, 1980; Mathews, 1991), fuzzy inference systems (Jang and Sun, 1993), generalised linear models (Nelder and Wedderburn, 1972), mu”
Neural Network Approaches to Image Compression (1995, background): “6 was proposed in 1989 by Pao [24] who introduced a functional-link network that allows a multilayer perceptron to be replaced by a singlelayer network employing higher-order terms.”
Identification and Prediction of Dynamic Systems Using an Interactively Recurrent Self-Evolving Fuzzy Neural Network (2013, methodology): “The functional link artificial neural network (FLANN) is basically a single layer structure in which nonlinearity is Fig.”
Legendre Neural Network for nonlinear Active Noise Cancellation with nonlinear secondary path (2011, methodology): “In order to reduce the training time of the NN and to improve its convergence rate, an alternate NN structure called functional link NN (FLANN) was proposed by Pao[8].”
Backpropagation and recurrent neural networks in financial analysis of multiple stock market returns (1996, methodology): “A backpropagation neural network implemented by Pao [ 6 ] was used in these experiments.”
Machine Learning Algorithms in Civil Structural Health Monitoring: A Systematic Review (2020, background): “Artificial Intelligence emerged between the 1950s and 1970s in the field of computer science and achieved substantial success in various subfields such as robotics [14, 15], data mining [130], pattern recognition [94], knowledge representation [14, 15] and agent systems [128].”
Nonlinear dynamic system identification using Legendre neural network (2010, result): “have introduced functional-link ANN (FLANN), and shown that it is capable of producing similar performance as that of MLP but with much less computational cost [7], [8].”
Explanatory rule extraction based on the trained neural network and the genetic programming (2006, methodology): “Neural networks are mathematical representations inspired by the functioning of the human brain [5][21].”
Small sample size generalization (1995, background): “Training rules for perceptrons and neural network classifiers belong to this family, see Pao [8].”
Short-term hourly load forecasting using time-series modeling with peak load estimation capability (2001, methodology): “This calculation is relatively similar to training phase of ANNs, but training of ANNs is done in successive iterations [ 19 ] and on the contrary parameter tuning of ARIMA is done in one step.”
Complex-Chebyshev Functional Link Neural Network Behavioral Model for Broadband Wireless Power Amplifiers (2012, methodology): “CCFLNN BEHAVIORAL MODEL OF PAs The FLNN, initially proposed by Pao [35], is a single-layer NN structure capable of performing function approximation and pattern classification.”