Neural assembly generation by selective connection weight updating

2010 ◽  
Author(s):  
Yoshitsugu Kakemoto ◽  
Shinichi Nakasuka
Keyword(s):  
Connection Weight ◽  
Neural Assembly

An excitatory neural assembly encodes working memory in the prefrontal cortex

2018 ◽  
Author(s):  
Yonglu Tian ◽  
Chaojuan Yang ◽  
Yaxuan Cui ◽  
Feng Su ◽  
Yongjie Wang ◽  
...  
Keyword(s):  
Working Memory ◽  
Prefrontal Cortex ◽  
Neural Assembly

Dual mass MEMS gyroscope temperature drift compensation based on TFPF-MEA-BP algorithm

Sensor Review ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Huiliang Cao ◽  
Rang Cui ◽  
Wei Liu ◽  
Tiancheng Ma ◽  
Zekai Zhang ◽  
...  
Keyword(s):  
Training Data ◽  
Experimental Result ◽  
Intrinsic Mode Functions ◽  
Temperature Drift ◽  
Connection Weight ◽  
Bp Network ◽  
Content Type ◽  
Mems Gyroscope ◽  
Time Frequency ◽  
Drift Compensation

Purpose To reduce the influence of temperature on MEMS gyroscope, this paper aims to propose a temperature drift compensation method based on variational modal decomposition (VMD), time-frequency peak filter (TFPF), mind evolutionary algorithm (MEA) and BP neural network. Design/methodology/approach First, VMD decomposes gyro’s temperature drift sequence to obtain multiple intrinsic mode functions (IMF) with different center frequencies and then Sample entropy calculates, according to the complexity of the signals, they are divided into three categories, namely, noise signals, mixed signals and temperature drift signals. Then, TFPF denoises the mixed-signal, the noise signal is directly removed and the denoised sub-sequence is reconstructed, which is used as training data to train the MEA optimized BP to obtain a temperature drift compensation model. Finally, the gyro’s temperature characteristic sequence is processed by the trained model. Findings The experimental result proved the superiority of this method, the bias stability value of the compensation signal is 1.279 × 10–3°/h and the angular velocity random walk value is 2.132 × 10–5°/h/vHz, which is improved compared to the 3.361°/h and 1.673 × 10–2°/h/vHz of the original output signal of the gyro. Originality/value This study proposes a multi-dimensional processing method, which treats different noises separately, effectively protects the low-frequency characteristics and provides a high-precision training set for drift modeling. TFPF can be optimized by SEVMD parallel processing in reducing noise and retaining static characteristics, MEA algorithm can search for better threshold and connection weight of BP network and improve the model’s compensation effect.

scholarly journals Application of Artificial Neural Network to Improve Pleurotus sp. Cultivation Modelling

2019 ◽  
Vol 255 ◽  
pp. 02010
Author(s):  
Fakharudin Abdul Sahli ◽  
Zainol Norazwina ◽  
Dzulkefli Noor Athirah
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Mathematical Modelling ◽  
Nitrogen Concentration ◽  
Network Modelling ◽  
Connection Weight ◽  
Palm Fruit ◽  
Cultivation Process ◽  
Artificial Neural ◽  
Trained Network

Mathematical modelling for nitrogen concentration in mycelium (N) during Pleurotus sp. cultivation had successfully been produced using multiple linear regression. Two different substrates were used to cultivate the Pleurotus sp. which were empty palm fruit bunch (EFB) and sugarcane bagasse (SB). Both substrates were collected and prepared as the selected factors which were type of substrate (SB - A and EFB - B), size of substrates (0.5 cm and 2.5 cm), mass ratio of spawn to substrate (SP/SS) (1:10 and 1:14), temperature during spawn running (25°C and ambient) and pre-treatment of substrates (steam and non-steam). The response was nitrogen concentration in mycelium (N). This paper presents the application of artificial neural network to improve the modelling process. Artificial neural network is one of the machine learning method which use the cultivation process information and extract the pattern from the data. Neural network ability to learn pattern by changing the connection weight had produced a trained network which represent the Pleurotus sp. cultivation process. Next this trained network was validated using error measurement to determine the modelling accuracy. The results show that the artificial neural network modelling produced better results with higher accuracy and lower error when compared to the mathematical modelling.

scholarly journals Pruning Multilayered ELM Using Cholesky Factorization and Givens Rotation Transformation

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Jingyi Liu ◽  
Xinxin Liu ◽  
Chongmin Liu ◽  
Ba Tuan Le ◽  
Dong Xiao
Keyword(s):  
Neural Network ◽  
Coal Industry ◽  
Feedforward Neural Network ◽  
Weight Matrix ◽  
Cholesky Factorization ◽  
Connection Weight ◽  
Givens Rotation ◽  
Rotation Transformation ◽  
Hidden Layer ◽  
Hidden Nodes

Extreme learning machine is originally proposed for the learning of the single hidden layer feedforward neural network to overcome the challenges faced by the backpropagation (BP) learning algorithm and its variants. Recent studies show that ELM can be extended to the multilayered feedforward neural network in which the hidden node could be a subnetwork of nodes or a combination of other hidden nodes. Although the ELM algorithm with multiple hidden layers shows stronger nonlinear expression ability and stability in both theoretical and experimental results than the ELM algorithm with the single hidden layer, with the deepening of the network structure, the problem of parameter optimization is also highlighted, which usually requires more time for model selection and increases the computational complexity. This paper uses Cholesky factorization strategy and Givens rotation transformation to choose the hidden nodes of MELM and obtains the number of nodes more suitable for the network. First, the initial network has a large number of hidden nodes and then uses the idea of ridge regression to prune the nodes. Finally, a complete neural network can be obtained. Therefore, the ELM algorithm eliminates the need to manually set nodes and achieves complete automation. By using information from the previous generation’s connection weight matrix, it can be evitable to re-calculate the weight matrix in the network simplification process. As in the matrix factorization methods, the Cholesky factorization factor is calculated by Givens rotation transform to achieve the fast decreasing update of the current connection weight matrix, thus ensuring the numerical stability and high efficiency of the pruning process. Empirical studies on several commonly used classification benchmark problems and the real datasets collected from coal industry show that compared with the traditional ELM algorithm, the pruning multilayered ELM algorithm proposed in this paper can find the optimal number of hidden nodes automatically and has better generalization performance.

Adaptive Synchronization in Unknown Stochastic Chaotic Neural Networks with Mixed Time-Varying Delays

2011 ◽  
pp. 289-313
Author(s):  
Jian-an Fang ◽  
Yang Tang
Keyword(s):  
Neural Networks ◽  
Sufficient Conditions ◽  
Stochastic Perturbation ◽  
Adaptive Synchronization ◽  
Output Coupling ◽  
Time Varying ◽  
Connection Weight ◽  
Chaotic Neural Networks ◽  
Simulation Results ◽  
Feedback Techniques

Neural networks (NNs) have been useful in many fields, such as pattern recognition, image processing etc. Recently, synchronization of chaotic neural networks (CNNs) has drawn increasing attention due to the high security of neural networks. In this chapter, the problem of synchronization and parameter identification for a class of chaotic neural networks with stochastic perturbation via state and output coupling, which involve both the discrete and distributed time-varying delays has been investigated. Using adaptive feedback techniques, several sufficient conditions have been derived to ensure the synchronization of stochastic chaotic neural networks. Moreover, all the connection weight matrices can be estimated while the lag synchronization and complete synchronization is achieved in mean square at the same time. The corresponding simulation results are given to show the effectiveness of the proposed method.

Finite-time boundedness for time-delay neural networks with external disturbances via weight learning approaches

2019 ◽  
Vol 33 (28) ◽  
pp. 1950343 ◽  
Author(s):  
Zhilian Yan ◽  
Youmei Zhou ◽  
Xia Huang ◽  
Jianping Zhou
Keyword(s):  
Neural Networks ◽  
Time Delay ◽  
Finite Time ◽  
Weight Matrix ◽  
External Disturbances ◽  
Connection Weight ◽  
Learning Rules ◽  
Finite Time Boundedness ◽  
Weight Learning ◽  
Matrix Case

This paper addresses the issue of finite-time boundedness for time-delay neural networks with external disturbances via weight learning. With the help of a group of inequalities and combining with the Lyapunov theory, weight learning rules are devised to ensure the neural networks to be finite-time bounded for the fixed connection weight matrix case and the fixed delayed connection weight matrix case, respectively. Sufficient conditions on the existence of the desired learning rules are presented in the form of linear matrix inequalities, which are easily verified by MATLAB software. It is shown that the proposed learning rules also guarantee the finite-time stability of the time-delay neural networks. Finally, a numerical example is employed to show the applicability of the devised weight learning rules.

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