This paper investigates the vehicle platoon control problems with both velocity constraints and input saturation. Firstly, radial basis function neural networks are employed to approximate the unknown driving resistance of a vehicle’s dynamic model. Then, a bidirectional topology, where vehicles can only communicate with their direct preceding and following neighbors, is used to depict the relationship among the vehicles in the platoon. On this basis, a neural adaptive sliding-mode control algorithm with an anti-windup compensation technique is proposed to (...) maintain the vehicle platoon with desired distance. Moreover, the string stability and the strong string stability of the whole vehicle platoon are proven through the stability theorem. Finally, numerical simulations verify the feasibility and effectiveness of the proposed control method. (shrink)

This simulation study is a fuzzy model-based neural network control method. The basic idea is to consider the application of a special type of neural networks based on radial basis function, which belongs to a class of associative memory neural networks. The novelty of this approach is the use of an RBF neural network controller in a model reference adaptive control architecture, based on a one-step-ahead Takagi–Sugeno fuzzy model. The objective is to control the concentration in (...) a continuous stirred-tank reactor highly non-linear system and to assure its stability by limiting the temperature rise generated from the irreversible exothermic reaction. This contribution will help to reduce environmental impact of chemical waste. (shrink)

An adaptive fractional-order nonsingular terminal sliding mode controller for a microgyroscope is presented with uncertainties and external disturbances using a fuzzy neural network compensator based on a backstepping technique. First, the dynamic of the microgyroscope is transformed into an analogical cascade system to guarantee the application of a backstepping design. Then, a fractional-order nonsingular terminal sliding mode surface is designed which provides an additional degree of freedom, higher precision, and finite convergence without a singularity problem. The proposed control scheme (...) requires no prior knowledge of the unknown dynamics of the microgyroscope system since the fuzzy neural network is utilized to approximate the upper bound of the lumped uncertainties and adaptive algorithms are derived to allow online adjustment of the unknown system parameters. The chattering phenomenon can be reduced simultaneously by the fuzzy neural network compensator. The stability and finite time convergence of the system can be established by the Lyapunov stability theorem. Finally, simulation results verify the effectiveness of the proposed controller and the comparison of root mean square error between different fractional orders and integer order is given to signify the high precision tracking performance of the proposed control scheme. (shrink)

Due to the excellent approximation ability, the neural networks based control method is used to achieve adaptive consensus of the fractional-order uncertain nonlinear multiagent systems with external disturbance. The unknown nonlinear term and the external disturbance term in the systems are compensated by using the radial basis function neural networks method, a corresponding fractional-order adaption law is designed to approach the ideal neural network weight matrix of the unknown nonlinear terms, and a control law is designed eventually. (...) According to the designed Lyapunov candidate function and the fractional theory, the systems stability is proved, and the adaptive consensus can be guaranteed by using the designed control law. Finally, two simulations are shown to illustrate the validity of the obtained results. (shrink)

In recent years, neural networks have attracted much attention in the machine learning and the deep learning technologies. Bio-inspired functions and intelligence are also expected to process efficiently and improve existing technologies. In the visual pathway, the prominent features consist of nonlinear characteristics of squaring and rectification functions observed in the retinal and visual cortex networks, respectively. Further, adaptation is an important feature to activate the biological systems, efficiently. Recently, to overcome short-comings of the deep learning techniques, orthogonality (...) for the weights in the networks has been developed for the signal propagation and the efficient optimization of the learning. In this paper, bio-inspired asymmetric networks with nonlinear characteristics are proposed, which are derived from the retinal networks in the biological visual pathway. The asymmetric network proposed here was verified to detect the movement of the object, efficiently in our previous studies. This paper shows a new characteristic of the adaptive orthogonality in the asymmetric networks. First, it is shown that the asymmetric network with nonlinear characteristics is effective for generating orthogonality. Second, the proposed asymmetric network with Gabor filters is compared with the conventional energy model from the point of the orthogonality characteristics. Finally, the asymmetric networks with nonlinear characteristics can generate the extended orthogonal bases in independent subspaces, which are useful for classification and efficient learning. (shrink)

An adaptive neural network control problem is addressed for a class of nonlinear hydraulic servo-systems with time-varying state constraints. In view of the low precision problem of the traditional hydraulic servo-system which is caused by the tracking errors surpassing appropriate bound, the previous works have shown that the constraint for the system is a good way to solve the low precision problem. Meanwhile, compared with constant constraints, the time-varying state constraints are more general in the actual systems. Therefore, when (...) the states of the system are forced to obey bounded time-varying constraint conditions, the high precision tracking performance of the system can be easily realized. In order to achieve this goal, the time-varying barrier Lyapunov function is used to prevent the states from violating time-varying constraints. By the backstepping design, the adaptive controller will be obtained. A radial basis function neural network is used to estimate the uncertainties. Based on analyzing the stability of the hydraulic servo-system, we show that the error signals are bounded in the compacts sets; the time-varying state constrains are never violated and all singles of the hydraulic servo-system are bounded. The simulation and experimental results show that the tracking accuracy of system is improved and the controller has fast tracking ability and strong robustness. (shrink)

Adaptive training adjusts a training task with the goal of improving learning outcomes. Adaptive training has been shown to improve human performance in attention, working memory capacity, and motor control tasks. Additionally, correlations have been observed between neural EEG spectral features and the performance of some cognitive tasks. This relationship suggests some EEG features may be useful in adaptive training regimens. Here, we anticipated that adding a neural measure into a behavioral-based adaptive training system would improve human performance (...) on a subsequent transfer task. We designed, developed, and conducted a between-subjects study of 44 participants comparing three training regimens: Single Item Fixed Difficulty, Behaviorally Adaptive Training, and Combined Adaptive Training using both behavioral and EEG measures. Results showed a statistically significant transfer task performance advantage of the CAT-based system relative to SIFD and BAT systems of 6 and 9 percentage points, respectively. Our research shows a promising pathway for designing closed-loop BCI systems based on both users' behavioral performance and neural signals for augmenting human performance. (shrink)

This paper proposes an adaptive neural network control approach for a direct-current system with full state constraints. To guarantee that state constraints always remain in the asymmetric time-varying constraint regions, the asymmetric time-varying Barrier Lyapunov Function is employed to structure an adaptive NN controller. As we all know that the constant constraint is only a special case of the time-varying constraint, hence, the proposed control method is more general for dealing with constraint problem as compared with the existing works (...) on DC systems. As far as we know, this system is the first studied situations with time-varying constraints. Using Lyapunov analysis, all signals in the closed-loop system are proved to be bounded and the constraints are not violated. In this paper, the effectiveness of the control method is demonstrated by simulation results. (shrink)

This paper presents an adaptive neural tracking control approach for a two-joint robotic manipulator with unknown time-varying delays. In order to work out the effect of unknown time-varying delays on the two-joint robotic manipulator, the appropriate Lyapunov–Krasovskii functionals and separation technology are chosen to settle this matter. The neural networks work as an approximator that has the advantage of estimating the unknown function in the system. In this paper, Lyapunov stability analysis can prove that all signals of the (...) closed-loop system are semiglobal uniformly ultimately bounded and the tracking error can converge to a compact neighborhood with respect to zero. The simulation consequences demonstrate the availability of the feedforward control approach. (shrink)

This paper focuses on modeling and control of a class of serial variable stiffness actuators based on level mechanisms for robotic applications. A multi-input multi-output complex nonlinear dynamic model is derived to fully describe SVSAs and the relative degree of the model is determined accordingly. Due to nonlinearity, high coupling, and parametric uncertainty of SVSAs, a neural network-based adaptive control strategy based on feedback linearization is proposed to handle system uncertainties. The feasibility of the proposed approach for position and (...) stiffness tracking of SVSAs is verified by simulation results. (shrink)

Emotion recognition plays an important part in human-computer interaction. Currently, the main challenge in electroencephalogram -based emotion recognition is the non-stationarity of EEG signals, which causes performance of the trained model decreasing over time. In this paper, we propose a two-level domain adaptation neural network to construct a transfer model for EEG-based emotion recognition. Specifically, deep features from the topological graph, which preserve topological information from EEG signals, are extracted using a deep neural network. These features are (...) then passed through TDANN for two-level domain confusion. The first level uses the maximum mean discrepancy to reduce the distribution discrepancy of deep features between source domain and target domain, and the second uses the domain adversarial neural network to force the deep features closer to their corresponding class centers. We evaluated the domain-transfer performance of the model on both our self-built data set and the public data set SEED. In the cross-day transfer experiment, the ability to accurately discriminate joy from other emotions was high: sadness, anger, and fear on the self-built data set. The accuracy reached 74.93% on the SEED data set. In the cross-subject transfer experiment, the ability to accurately discriminate joy from other emotions was equally high: sadness, anger, and fear on the self-built data set. The average accuracy reached 87.9% on the SEED data set, which was higher than WGAN-DA. The experimental results demonstrate that the proposed TDANN can effectively handle the domain transfer problem in EEG-based emotion recognition. (shrink)

Relying on large-scale parallel corpora, neural machine translation has achieved great success in certain language pairs. However, the acquisition of high-quality parallel corpus is one of the main difficulties in machine translation research. In order to solve this problem, this paper proposes unsupervised domain adaptive neural network machine translation. This method can be trained using only two unrelated monolingual corpora and obtain a good translation result. This article first measures the matching degree of translation rules by adding relevant (...) subject information to the translation rules and dynamically calculating the similarity between each translation rule and the document to be translated during the decoding process. Secondly, through the joint training of multiple training tasks, the source language can learn useful semantic and structural information from the monolingual corpus of a third language that is not parallel to the current two languages during the process of translation into the target language. Experimental results show that better results can be obtained than traditional statistical machine translation. (shrink)

A familiar trope of cognitive science, linguistics, and the philosophy of psychology over the past forty or so years has been the idea of the mind as a modular system-that is, one consisting of functionally specialized subsystems responsible for processing different classes of input, or handling specific cognitive tasks like vision, language, logic, music, and so on. However, one of the major achievements of neuroscience has been the discovery that the brain has incredible powers of renewal and reorganization. This "neuroplasticity," (...) in its various forms, has challenged many of the orthodox conceptions of the mind which originally led cognitive scientists to postulate hardwired mental modules. -/- This book examines how such discoveries have changed the way we think about the structure of the mind. It contends that the mind is more supple than prevailing theories in cognitive science and artificial intelligence acknowledge. The book uses language as a test case. The claim that language is cognitively special has often been understood as the claim that it is underpinned by dedicated-and innate-cognitive mechanisms. Zerilli offers a fresh take on how our linguistic abilities could be domain-general: enabled by a composite of very small and redundant cognitive subsystems, few if any of which are likely to be specialized for language. In arguing for this position, however, the book takes seriously various cases suggesting that language dissociates from other cognitive faculties. -/- Accessibly written, The Adaptable Mind is a fascinating account of neuroplasticity, neural reuse, the modularity of mind, the evolution of language, and faculty psychology. (shrink)

Water quality prediction is the basis of water environmental planning, evaluation, and management. In this work, a novel intelligent prediction model based on the fuzzy wavelet neural network including the neural network, the fuzzy logic, the wavelet transform, and the genetic algorithm was proposed to simulate the nonlinearity of water quality parameters and water quality predictions. A self-adapted fuzzy c-means clustering was used to determine the number of fuzzy rules. A hybrid learning algorithm based on a genetic algorithm (...) and gradient descent algorithm was employed to optimize the network parameters. Comparisons were made between the proposed FWNN model and the fuzzy neural network, the wavelet neural network, and the neural network. The results indicate that the FWNN made effective use of the self-adaptability of NN, the uncertainty capacity of FL, and the partial analysis ability of WT, so it could handle the fluctuation and the nonseasonal time series data of water quality, while exhibiting higher estimation accuracy and better robustness and achieving better performances for predicting water quality with high determination coefficients R2 over 0.90. The FWNN is feasible and reliable for simulating and predicting water quality in river. (shrink)

Prism adaptation is a form of visuomotor training that produces both sensorimotor and cognitive aftereffects depending on the direction of the visual displacement. Recently, a neural framework explaining both types of PA-induced aftereffects has been proposed, but direct evidence for it is lacking. We employed Structural Equation Modeling, a form of effective connectivity analysis, to establish directionality among connected nodes of the brain network thought to subserve PA. The findings reveal two distinct network branches: a loop involving connections (...) from the parietal cortices to the right parahippocampal gyrus, and a branch linking the lateral premotor cortex to the parahippocampal gyrus via the cerebellum. Like the sensorimotor aftereffects, the first branch exhibited qualitatively different modulations for left versus right PA, and critically, changes in these connections were correlated with the magnitude of the sensorimotor aftereffects. Like the cognitive aftereffects, changes in the second branch were qualitatively similar for left and right PA, with greater change for left PA and a trend correlation with cognitive aftereffects. These results provide direct evidence that PA is supported by two functionally distinct subnetworks, a parietal–temporal network responsible for sensorimotor aftereffects and a fronto-cerebellar network responsible for cognitive aftereffects. (shrink)

A dynamic learning method is developed for an uncertain n-link robot with unknown system dynamics, achieving predefined performance attributes on the link angular position and velocity tracking errors. For a known nonsingular initial robotic condition, performance functions and unconstrained transformation errors are employed to prevent the violation of the full-state tracking error constraints. By combining two independent Lyapunov functions and radial basis function neural network approximator, a novel and simple adaptive neural control scheme is proposed for the dynamics (...) of the unconstrained transformation errors, which guarantees uniformly ultimate boundedness of all the signals in the closed-loop system. In the steady-state control process, RBF NNs are verified to satisfy the partial persistent excitation condition. Subsequently, an appropriate state transformation is adopted to achieve the accurate convergence of neural weight estimates. The corresponding experienced knowledge on unknown robotic dynamics is stored in NNs with constant neural weight values. Using the stored knowledge, a static neural learning controller is developed to improve the full-state tracking performance. A comparative simulation study on a 2-link robot illustrates the effectiveness of the proposed scheme. (shrink)

This article investigates an adaptive neural network control algorithm for marine surface vessels with time-varying output constraints and unknown external disturbances. The nonlinear state-dependent transformation is introduced to eliminate the feasibility conditions of virtual controller. Moreover, the barrier Lyapunov function is used to achieve time-varying output constraints. As an important approximation tool, the NN is employed to approximate uncertain and continuous functions. Subsequently, the disturbance observer is structured to observe time-varying constraints and unknown external disturbances. The novel strategy can (...) guarantee that all signals in the closed-loop system are semiglobally uniformly ultimately bounded. Finally, the simulation results verify the benefit of the proposed method. (shrink)

This study is to explore the optimization of the adaptive genetic algorithm in the backpropagation neural network, so as to expand the application of the BPNN model in nonlinear issues. Traffic flow prediction is undertaken as a research case to analyse the performance of the optimized BPNN. Firstly, the advantages and disadvantages of the BPNN and genetic algorithm are analyzed based on their working principles, and the AGA is improved and optimized. Secondly, the optimized AGA is applied to optimize (...) the standard BPNN, and the optimized algorithm is named as OAGA-BPNN. Finally, three different cases are proposed based on the actual scenario of traffic flow prediction to analyse the optimized algorithm on the matrix laboratory platform by simulation. The results show that the average error distribution of the GA-BPNN algorithm is about 1% with small fluctuation range, better calculation accuracy, and generalization performance in contrast to the BPNN. The average output error of the AGA-BPNN fluctuates around 0 and remains in a relatively stable range as a whole in contrast to that of GA-BPNN; the maximum fitness level keeps increasing during the evolution process but approaches the average value in later process, so the population diversity is hard to be guaranteed. The output error of the OAGA-BPNN fluctuates little compared with that of AGA-BPNN, and its maximum fitness continues to increase in the evolution process with guaranteed population diversity. In short, the OAGA-BPNN algorithm can achieve the best performance in terms of calculation accuracy, generalization performance, and population evolution. (shrink)

In the modern scientific literature, there are many reports about the successful application of neural network technologies for solving complex applied problems, in particular, for modeling the urban real estate market. There are neural network models that can perform mass assessment of real estate objects taking into account their construction and operational characteristics. However, these models are static because they do not take into account the changing economic situation over time. Therefore, they quickly become outdated and need frequent (...) updates. In addition, if they are designed for a specific city, they are not suitable for other cities. On the other hand, there are several dynamic models taking into account the overall state of the economy and designed to predict and study the overall price situation in real estate markets. Such dynamic models are not intended for mass real estate appraisals. The aim of this article is to develop a methodology and create a complex model that has the properties of both static and dynamic models. Moreover, our comprehensive model should be suitable for evaluating real estate in many cities at once. This aim is achieved since our model is based on a neural network trained on examples considering both construction and operational characteristics, as well as geographical and environmental characteristics, along with time-changing macroeconomic parameters that describe the economic state of a specific region, country, and the world. A set of examples for training and testing the neural network were formed on the basis of statistical data of real estate markets in a number of Russian cities for the period from 2006 to 2020. Thus, many examples included the data relating to the periods of the economic calm for Russia, along with the periods of crisis, recovery, and growth of the Russian and global economy. Due to this, the model remains relevant with the changes of the international economic situation and it takes into account the specifics of regions. The model proved to be suitable for solving the following tasks: industrial economic analysis, company strategic and operational management, analytical and consulting support of investment, and construction activities of professional market participants. The model can also be used by government agencies authorized to conduct public cadastral assessment for calculating property taxes. (shrink)

Neural network models have recently made significant achievements in solving vehicle scheduling problems. Adaptive ant colony algorithm provides a new idea for neural networks to solve complex system problems of multiconstrained network intensive vehicle routing models. The pheromone in the path is changed by adjusting the volatile factors in the operation process adaptively. It effectively overcomes the tendency of the traditional ant colony algorithm to fall easily into the local optimal solution and slow convergence speed to search for (...) the global optimal solution. The multiconstrained network intensive vehicle routing algorithm based on adaptive ant colony algorithm in this paper refers to the interaction between groups. Adaptive transfer and pheromone update strategies are introduced based on the traditional ant colony algorithm to optimize the selection, update, and coordination mechanisms of the algorithm further. Thus, the search task of the objective function for a feasible solution is completed by the search ants. Through the division and collaboration of different kinds of ants, pheromone adaptive strategy is combined with polymorphic ant colony algorithm. It can effectively overcome some disadvantages, such as premature stagnation, and has a theoretical significance to the study of large-scale multiconstrained vehicle routing problems in complex traffic network systems. (shrink)

In this work, a new intelligent control strategy based on neural networks is proposed to cope with some external disturbances that can affect quadrotor unmanned aerial vehicles dynamics. Specifically, the variation of the system mass during logistic tasks and the influence of the wind are considered. An adaptive neuromass estimator and an adaptive neural disturbance estimator complement the action of a set of PID controllers, stabilizing the UAV and improving the system performance. The control strategy has been extensively (...) tested with different trajectories: linear, helical, circular, and even a lemniscate one. During the experiments, the mass of the UAV is triplicated and winds of 6 and 9 in Beaufort’s scale are introduced. Simulation results show how the online learning of the estimator increases the robustness of the controller, reducing the effects of the changes in the mass and of the wind on the quadrotor. (shrink)