Fenxi Yao

Verification and predicting temperature and humidity in a solar greenhouse based on convex bidirectional extreme learning machine algorithm

Predictions regarding the solar greenhouse temperature and humidity are important because they play a critical role in greenhouse cultivation. On account of this, it is important to set up a predictive model of temperature and humidity that would precisely predict the temperature and humidity, reducing potential financial losses. This paper presents a novel temperature and humidity prediction model based on convex bidirectional extreme learning machine (CB-ELM). Simulation results show that the convergence rate of the bidirectional extreme learning machine (B-ELM) can further be improved while retaining the same simplicity, by simply recalculating the output weights of the existing nodes based on a convex optimization method when a new hidden node is randomly added. The performance of the CB-ELM model is compared with other modeling approaches by applying it to predict solar greenhouse temperature and humidity. The experiment results show that the CB-ELM model predictions are more accurate than those of the B-ELM, Back Propagation Neural Network (BPNN), Support Vector Machine (SVM), and Radial Basis Function (RBF). Therefore, it can be considered as a suitable and effective method for predicting the solar greenhouse temperature and humidity.

Improved Meta-ELM with error feedback incremental ELM as hidden nodes

Liao et al. (Neurocomputing 128:81–87, 2014) proposed a meta-learning approach to extreme learning machine (Meta-ELM), which can obtain good generalization performance by training multiple ELMs. However, one of its open problems is overfitting when minimizing training error. In this paper, we propose an improved meta-learning model of ELM (improved Meta-ELM) to handle the problem. The improved Meta-ELM architecture is composed of some base ELMs which are error feedback incremental extreme learning machine (EFI-ELM) and the top ELM. The improved Meta-ELM includes two stages. First, each base ELM with EFI-ELM is trained on a subset of training data. Then, the top ELM learns with the base ELMs as hidden nodes. Simulation results on some artificial and benchmark datasets show that the proposed improved Meta-ELM model is more feasible and effective than Meta-ELM.

Back Propagation Convex Extreme Learning Machine

Recently, extreme learning machine has greatly improved in training speed and learning effectiveness of feedforward neural network which includes one hidden layer. However, the random initialization of ELM model parameters can bring randomness and affect generalization ability. The paper proposed back propagation convex extreme learning machine (BP-CELM), in which the hidden layer parameters \( {\mathbf{(a}},\,{\mathbf{b}}) \) can be calculated by formulas. The convergence of BP-CELM is proved in the paper. Simulation results show that BP-CELM has higher training speed and better generalization performance than other randomized neural network algorithms.

Modified bidirectional extreme learning machine with Gram–Schmidt orthogonalization method

Abstract Incremental extreme learning machine has been proved to be an efficient and simple universal approximator. However, the network architecture may be very large due to the inefficient nodes which have a tiny effect on reducing the residual error. More to the point, the output weights are not the least square solution. To reduce such inefficient nodes, a method called bidirectional ELM (B-ELM), which analytically calculates the input weights of even nodes, was proposed. By analyzing, B-ELM can be further improved to achieve better performance on compacting structure. This paper proposes the modified B-ELM (MB-ELM), in which the orthogonalization method is involved in B-ELM to orthogonalize the output vectors of hidden nodes and the resulting vectors are taken as the output vectors. MB-ELM can greatly diminish inefficient nodes and obtain a preferable output weight vector which is the least square solution, so that it has better convergence rate and a more compact network architecture. Specifically, it has been proved that in theory, MB-ELM can reduce residual error to zero by adding only two nodes into network. Simulation results verify these conclusions and show that MB-ELM can reach smaller low limit of residual error than other I-ELM methods.

Distributed model predictive control of linear systems with unmeasurable states and uncertain parameters

Distributed model predictive control (DMPC) is widely used in complex industrial process control. The theoretical researches of DMPC have got more and more attention because of its good performances, such as the ability of dealing with all kinds of constraints effectively, high flexibility and fault tolerance. In this paper, the linear systems with uncertain parameters and unmeasurable states are confirmed by generalized polynomial chaos expansion method. Then the DMPC algorithm is realized by using the state observers to estimate states.

Design and implementation of a networked controller based on state prediction

Aiming at the problem of random delay in close-loop networked control systems, this study designed a LQ control strategy based on state prediction. Time-variant transmission delays can be transformed into fixed delays by adding data buffers to the controller and the actuator. Accordingly, time-variant stochastic systems can be transformed into deterministic systems. In this study, controllability of deterministic networked control systems is systematically analyzed, and a LQ control scheme based on state prediction is studied. Also the validity of the algorithm for the controller is demonstrated by practical experiments.