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In QAM communication system, CMA only was utilized to module statistical property of signals, and not to contain the phase information. In the phase deviation channel, great phase error was brought out. Simultaneously, it affected the convergence rate. A restraint function utilizing the amplitude of the signal was constructed in this article. The function must approach zero when the amplitude was...
In the area of artificial neural networks, the Back Propagation (BP) learning algorithm has proved to be efficient in many engineering applications especially in pattern recognition, signal processing and system control. Although the BP learning has been a significant research area of neural network, it has also been known as an algorithm with a poor convergence rate. Many attempts have been made...
Early warning systems are critical in providing emergency response in the event of unexpected hazards. Cheap cameras and improvements in memory and computing power have enabled the design of fire detectors using video surveillance systems. This is critical in scenarios where traditional smoke detectors cannot be installed. In such scenarios, it has been observed that the smoke is visible well before...
Extreme learning machine (ELM) is one of the effective training algorithms for single hidden layer feedforward neural networks (SLFNs), but it often requires a large number of hidden units which makes the trained networks respond slowly to input patterns. Regularized least-squares extreme learning machine (RLS-ELM) is one of the improvements which can overcome this problem. It determines the input...
BP algorithm solves how to change hidden neurons weights of multilayer feed-forward neural networks, it uses mean square error criterion as the cost function, which takes gradient descent method to optimize the cost function to get the minimum and propagate the error signals to tune the weights. The gradient descent method uses fixed learning rate which denotes the weights changing extent. If the...
In the last decade, the use of artificial neural networks (ANN) has become widely accepted in medical applications for accuracy for predictive inference, with potential to support and flexible non-linear modelling of large data sets. Feedforward neural network (FNN) is a kind of artificial neural networks, which has a better structure and been widely used. But there are still many drawbacks if we...
Multipath and limited bandwidth are two major impairments in the digital communication system. This leads to intersymbol interference (ISI) at the receiver. In this paper, a new blind equalization algorithm based on the fuzzy neural network (FNN) controller is proposed. Because the unlinear modify factor in the blind equalization algorithm based on the feed-forward neural network (FFNN) may affect...
Automatic target recognition (ATR) of aircrafts using translation invariant features derived from high range resolution (HRR) profiles and multilayered neural network is presented in this paper. The HRR profile sequences are translation variant in the range resolution cell because of the non-cooperative target maneuvering. The differential power spectrum (DPS) is introduced to extract the translation...
In recent years, the multilayer feedforward neural network (FNN) has been received considerable attention and have been extensively used in many fields. Levenberg-Marquardt back-propagation (LMBP) algorithm as an FNN training method has some limitations associated with overfitting, local optimum problems and slow convergence rate. In order to overcome the limitations, some people proposed particle...
Artificial Neural Networks (ANNs) have been applied to machine condition monitoring. This paper first addresses a ANN trained by Group Search Optimizer (GSO), which is a novel population based optimization algorithm inspired by animal social foraging behaviour. The global search performance of GSO has been proven to be competitive to other evolutionary algorithms, such as Genetic Algorithms (GAs)...
Dynamic neural network algorithms are used for automatic network design in order to avoid time consuming search for finding an appropriate network topology with trial and error methods. The Cascade Correlation Network is a constructive method for building network architectures automatically. We present a novel incremental cascade network architecture based on it. We also report on benchmarking results...
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