A new bifurcation parameter in a modified Huber-Braun model

Toplinsko osjetljivi neuroni predstavljaju pulsirajuće-prskajuću aktivnost predskazanu brzim ponavljajućim iznenadnim razvojem događanja praćenim periodima spavanja. Sinhronizacija takvog ponašanja u mreži spojenih prskajućih neurona kao što je epileptogena zona u mozgu može dovesti do neuroloških poremećaja poput epileptičkih napada. U radu se predlaže novi pristup predviđanju napada modelom epileptičkog neurona. Parametri upotrebljeni u simulaciji izabrani su tako da se mogu potencijalno primijeniti u pristupima neinvazivnim stimulacijama mozga kao što je ponavljajuća transkranijalna magnetska stimulacija (rTMS). U tom pogledu, predstavlja se modificirani Huber-Braun model termalno osjetljivog neurona izloženog rTMS-induciranoj voltaži. Primjenjujući teoriju kaosa dijagram bifurkacije modificiranog Huber-Braun modela s novim parametrom bifurkacije primijenjen je za procjenu vremena u kojem dolazi do bifurkacije čime se omogućuje točnije predviđanje početka napada na temelju modificiranog modela.Thermally sensitive neurons represent a bursting-spiking activity that is indicated by rapid repetitive spiking trains of action potentials pursued by dormant periods. Synchronization of such behavior in a network of coupled spiking neurons such as the epileptogenic zone in the brain may cause some neurological disorders such as epileptic seizures. This paper introduces a new approach for predicting the seizure onset in a model of an epileptic neuron. The parameters which are used for simulations have been selected in such a way that they would be potentially applicable in the non-invasive brain stimulation approaches such as repetitive transcranial magnetic stimulation (rTMS). In this regard, a modified Huber-Braun model of a thermally sensitive neuron exposed to external rTMS-induced voltages is presented. Applying the chaos theory, the bifurcation diagram of a modified Huber-Braun model with a new bifurcation parameter is used to estimate the time at which the bifurcation takes place whereby allowing a more accurate prediction of the seizure onset based on the modified model

Adaptive Neural Stabilizing Controller for a Class of Mismatched Uncertain Nonlinear Systems by State and Output Feedback

In this paper, first, an adaptive neural network (NN) state-feedback controller for a class of nonlinear systems with mismatched uncertainties is proposed. By using a radial basis function NN (RBFNN), a bound of unknown nonlinear functions is approximated so that no information about the upper bound of mismatched uncertainties is required. Then, an observer-based adaptive controller based on RBFNN is designed to stabilize uncertain nonlinear systems with immeasurable states. The state-feedback and observer-based controllers are based on Lyapunov and strictly positive real-Lyapunov stability theory, respectively, and it is shown that the asymptotic convergence of the closed-loop system to zero is achieved while maintaining bounded states at the same time. The presented methods are more general than the previous approaches, handling systems with no restriction on the dimension of the system and the number of inputs. Simulation results confirm the effectiveness of the proposed methods in the stabilization of mismatched nonlinear systems

Evaluating the performance of a nonlinear active noise control system in enclosure

The optimization of a nonlinear adaptive multi-channel active noise control (ANIC) system in a rectangular enclosure using neural networks is investigated in this paper. The model of enclosure is obtained using modal analysis techniques, and the target bandwidth of the control system for global reduction of noise is selected to be 50-300Hz. Secondary path in modeled offline using multilayer perceptron (MLP), and standard back-propagation algorithm by choosing a multi-tone as an excitation signal. The simulation results assuming linear and nonlinear models of secondary path show that in single-channel case multilayer perceptron neural networks with FxBP algorithm have superior performance than FIR structure with FxLMS algorithm, and in multi-channel case their performance are comparable

Hybrid fuzzy deep neural network toward temporal-spatial-frequency features learning of motor imagery signals

Abstract Achieving an efficient and reliable method is essential to interpret a user’s brain wave and deliver an accurate response in biomedical signal processing. However, EEG patterns exhibit high variability across time and uncertainty due to noise and it is a significant problem to be addressed in mental task as motor imagery. Therefore, fuzzy components may help to enable a higher tolerance to noisy conditions. With the advent of Deep Learning and its considerable contributions to Artificial intelligence and data analysis, numerous efforts have been made to evaluate and analyze brain signals. In this study, to make use of neural activity phenomena, the feature extraction preprocessing is applied based on Multi-scale filter bank CSP. In the following, the hybrid series architecture named EEG-CLFCNet is proposed which extract the frequency and spatial features by Compact-CNN and the temporal features by the LSTM network. However, the classification results are evaluated by merging the fully connected network and fuzzy neural block. Here, the proposed method is further validated by the BCI competition IV-2a dataset and compare with two hyperparameter tuning methods, Coordinate-descent and Bayesian optimization algorithm. The proposed architecture that used fuzzy neural block and Bayesian optimization as tuning approach, results in better classification accuracy compared with the state-of-the-art literatures. As results shown, the remarkable performance of the proposed model, EEG-CLFCNet, and the general integration of fuzzy units to other classifiers would pave the way for enhanced MI-based BCI systems

Reconfigurable logic blocks based on a chaotic Chua circuit

To investigate morphable chaotic logic we have constructed, out of discrete circuitry, a chaotic logic block that can morph between all two input, one output logic gates. Additionally, we investigate the sensitivity of such a block to noise and have been able to formulate a method that demonstrates that the chaotic saddles of the inherent chaotic dynamics can be exploited to enhance the robustness of the logic functions with respect to noise

Nonlinear model predictive control of chemical processes with a Wiener identification approach

Some chemical plants such as pH neutralization process have highly nonlinear behavior. Such processes demand a powerful wiener identification approach based on neural networks for identification of the nonlinear part. In this paper, the pH neutralization process is identified with NN-based wiener identification method and two linear and nonlinear model predictive controllers with the ability of rejecting slowly varying unmeasured disturbances are applied. Simulation results show that the obtained wiener model has good capability to predict the step response of the process. Parameters of both linear and nonlinear model predictive controllers are tuned and the best obtained results are compared. For this purpose, different operating points are selected to have a wide range of operation for the nonlinear process. Simulation results show that the nonlinear controller has better performance without any overshoot in comparison with linear MPC and also less steady-state error in tracking the set -points

Sliding mode control revisited

Controller design for nonlinear systems in its general form is complicated and an open problem. Finding a solution to this problem becomes more complicated when unwanted terms, such as disturbance, are taken into account. To provide a robust design for a subclass of nonlinear systems, sliding mode controllers (SMCs) are used. These controllers have a systematic design procedure and can reject bounded disturbances and at the same time guarantee stability. The guaranteed stability is achieved by separating system states into two parts and assuming that the input to state stability (ISS) condition holds for internal dynamics. This condition restricts the applicability of the SMC and limits the system performance when the controller is designed based on that. In order to remove this restriction and improve the performance, the ISS condition has been relaxed in this study. The relaxation is performed by redesigning SMCs based on suggested Lyapunov functions. The proposed idea insures global asymptotic stability of the closed loop system and is used to revise different well-known SMCs such as conventional SMC, terminal SMC, non-singular terminal SMC, integral SMC, super-twisting SMC, and super-twisting integral SMC. Comparisons between conventional and revised versions are made using simulation to demonstrate excellence of the revisited controllers