In this paper, an improved complex-valued backpropagation algorithm with gain parameters is proposed. It is then employed to train a complex-valued feedforward neural network with one hidden layer. The well-trained co...
详细信息
ISBN:
(纸本)9781479914821
In this paper, an improved complex-valued backpropagation algorithm with gain parameters is proposed. It is then employed to train a complex-valued feedforward neural network with one hidden layer. The well-trained complex-valued neural network is finally applied to deal with the recognition problem of 26 hand gestures. The results of experiment clearly show that much better performance can be achieved by our improved complex-valued backpropagation algorithm than some existing methods.
The study of Newton's method in complex-valued neural networks faces many difficulties. In this paper, we derive Newton's method backpropagation algorithms for complex-valued holomorphic multilayer perceptrons...
详细信息
ISBN:
(纸本)9781479914821
The study of Newton's method in complex-valued neural networks faces many difficulties. In this paper, we derive Newton's method backpropagation algorithms for complex-valued holomorphic multilayer perceptrons, and investigate the convergence of the one-step Newton steplength algorithm for the minimization of real-valued complex functions via Newton's method. To provide experimental support for the use of holomorphic activation functions, we perform a comparison of using sigmoidal functions versus their Taylor polynomial approximations as activation functions by using the algorithms developed in this paper and the known gradient descent backpropagation algorithm. Our experiments indicate that the Newton's method based algorithms, combined with the use of polynomial activation functions, provide significant improvement in the number of training iterations required over the existing algorithms.
Complex-Valued Neural Networks (CVNNs) are Artificial Neural Networks (ANNs) which function using complex numbers - they have complex-valued parameters and accept complex-valued inputs. Phase-Based Neurons (PBNs) are ...
详细信息
Complex-Valued Neural Networks (CVNNs) are Artificial Neural Networks (ANNs) which function using complex numbers - they have complex-valued parameters and accept complex-valued inputs. Phase-Based Neurons (PBNs) are simple CVNNs that use for the internal weights complex numbers with the modulus 1, the only adaptable parameters being the phases of the weights. We present in this paper some limitations of the Continuous Phase-Based Neuron (CPBN) and describe the structure of a Feedforward Multilayer Phase-Based Neural Network (MLPBN) and its training using an adaptation of the backpropagation algorithm.
Stochastic weight update is a variant of error back-propagation algorithm for learning of artificial neural networks. It allows for efficient topology-independent implementation of backpropagation through time for rec...
详细信息
Stochastic weight update is a variant of error back-propagation algorithm for learning of artificial neural networks. It allows for efficient topology-independent implementation of backpropagation through time for recurrent networks. In stochastic weight update scenario, constant number of weights and neurons is randomly selected and updated. This is in contrast to the classical ordered update, where all weights/neurons are always updated. In this paper we will study performance of stochastic weight update on recurrent neural networks using concept of feedforward network with added recurrent neurons.
This paper presents application of complex neural network for calculating complex resonating frequency of microstrip patch antenna on superstrate. The results obtained from neural network agrees well with the theoreti...
详细信息
ISBN:
(纸本)9781479932672
This paper presents application of complex neural network for calculating complex resonating frequency of microstrip patch antenna on superstrate. The results obtained from neural network agrees well with the theoretical results.
In this study, modeling approach for interpretation of data logged from chemically field-effect transistor (CHEMFET) sensor is described. Firstly, backpropagation algorithm is used to train the proposed network by opt...
详细信息
In this study, modeling approach for interpretation of data logged from chemically field-effect transistor (CHEMFET) sensor is described. Firstly, backpropagation algorithm is used to train the proposed network by optimizing the parameters of the network. Then, by applying the optimized parameters obtained from the trained network, the feed forward neural network algorithm is implemented using C language for compatibility with 16-bit microcontroller board and the output is compared with the simulation output which has been simulated using MATLAB software. Initial findings showed that the neural the proposed method is able to provide excellence estimation of main ion concentration in mixed solution as well as capable to interpret and estimate the ion concentration in mixed solution.
This paper presents application of complex neural network for calculating complex resonating frequency of microstrip patch antenna on superstrate. The results obtained from neural network agrees well with the theoreti...
详细信息
This paper presents application of complex neural network for calculating complex resonating frequency of microstrip patch antenna on superstrate. The results obtained from neural network agrees well with the theoretical results.
In this paper a neural network learning method with lower and upper type-2 fuzzy weight adjustment is proposed. The general mathematical analysis of the proposed learning method architecture and the adaptation of the ...
详细信息
ISBN:
(纸本)9781467361279
In this paper a neural network learning method with lower and upper type-2 fuzzy weight adjustment is proposed. The general mathematical analysis of the proposed learning method architecture and the adaptation of the interval type-2 fuzzy weights are presented. The proposed method is based on research of recent methods that manage weight adaptation and especially type-2 fuzzy weights. In this paper the neural network architecture managing lower and upper type-2 fuzzy weights and the obtained lower and upper final results are presented. The proposed approach is applied to a case of Mackey-Glass time series prediction.
In this paper the lower and upper type-2 fuzzy weight adjustment applied in a neural network performing the learning method is proposed. The mathematical representation of the adaptation of the interval type-2 fuzzy w...
详细信息
ISBN:
(纸本)9781479903467
In this paper the lower and upper type-2 fuzzy weight adjustment applied in a neural network performing the learning method is proposed. The mathematical representation of the adaptation of the interval type-2 fuzzy weights and the proposed learning method architecture are presented. This research is based in the analysis of the recent methods that manage weight adaptation and implementing this analysis in the adaptation of these methods with type-2 fuzzy weights. In this paper, we work with type-2 fuzzy weights lower and upper in the neural network architecture and the lower and upper final results obtained are presented in the final. The proposed approach is applied to a case of Mackey-Glass time series prediction.
In this study, a method to improve selectivity of chemically field-effect transistor (CHEMFET) sensor towards the main ion concentration in mixed solution is discussed. The approach is based on artificial neural netwo...
详细信息
In this study, a method to improve selectivity of chemically field-effect transistor (CHEMFET) sensor towards the main ion concentration in mixed solution is discussed. The approach is based on artificial neural network (ANN) as a post processing stage that performs the estimation of ion concentration in the mixed solution. Here, the algorithm developed will be able to estimate the main ion in mixed solution by learning the pattern of the input and output based on sensor reading extracted. Firstly, backpropagation algorithm is used to train proposed network by optimizing the parameters of the network. Initial findings showed that the performance of MLP architectures with backpropagation algorithm is able to provide excellence estimation of main ion concentration in mixed solution.
暂无评论