Weight and Structure Determination Neural Network Aided With Double Pseudoinversion for Diagnosis of Flat Foot

Chen, Liangming; Huang, Zhiguan; Li, Yuhe; Zeng, Nianyin; Liu, Mei; Peng, Anjie; Jin, Long

doi:10.1109/access.2019.2903634

Cited by 30 publications

(9 citation statements)

References 44 publications

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“…Features of biomechanics of the foot, which is the most important link in the general myo-fascial kinematic chains, largely determine the biomechanics of movements of the lower extremities, spine and human body as a whole [11,12,13].…”

Section: Discussionmentioning

confidence: 99%

Influence of lower leg myofascial kinematic chains on flat feet development of children 7-14 years old

Danyschuk¹

2019

HSR

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The aim of the work is to study the biomechanical properties of the myo-fascial kinematic chain "foot-shin" of children of 7-14 years old with non-fixed and clinically expressed flat-footedness.Material and methods. The study involved 14 children with flat-footedness of grades I-II and 6 children with flat-foot deformity of the foot and 20 children who only had functional disorders of the foot. An anthropometric study of the foot was carried out, electrophysiological indicators of the muscles of the leg were determined, and plantograms were analyzed.Results. The study found a correlation between the indicators of the anatomical and functional state of the foot and the imbalance of the frequency-amplitude indices of the ipsi and contralateral muscles within one link of the myofacial kinematic chain, may be important as one of the factors that contribute to the development of flatfoot. This is confirmed by other indicators and indicate a decrease in the height of the longitudinal arch, a decrease in the metatarsal and heel angles of the arch of the foot. Such changes have a pronounced relationship with age. The results of the work indicate that a possible cause of flattening of the vaulted apparatus of the foot is not only the weakness of its joint-ligament-muscular system, but also above the located kinematic segment - the tibia. The correlation analysis revealed the relationship between the indicators of the development of the anatomical and biomechanical components of the foot and the characteristics of the electromyographic indicators of the muscles of the leg in children 7-14 years old. As a result of a comprehensive study, it was found that during this period of ontogenesis in the formation of flatfoot such electromyographic indicators as frequency-amplitude characteristics of action potentials of motor units of the long and posterior tibial muscles, as well as their tone imbalance, take on major importance.Conclutions. Experimental studies have established that the registered changes in the articular components of the foot of children 7-14 years old lead to a change in the electromyographic parameters of the muscles of the leg, which are involved in the formation of the initial sections of myo-fascial kinematic chains.

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Section: Discussionmentioning

confidence: 99%

Influence of lower leg myofascial kinematic chains on flat feet development of children 7-14 years old

Danyschuk¹

2019

HSR

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“…As a result, the HLNs of the K-order TP NN employ the AF of (3.6) to generate sigmoidal activation. It is worth mentioning that several AFs, such as Chebyshev and Euler polynomials, sine, square wave, and power are employed on WASD-based NN in [15,28,29].…”

Section: Wdd Methods and Afmentioning

confidence: 99%

Feedback Control Systems Stabilization Using a Bio-inspired Neural Network

Mourtas

Katsikis

Kasimis

2022

EAI Endorsed Trans AI Robotics

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The proportional–integral–derivative (PID) control systems, which have become a standard for technical and industrial applications, are the fundamental building blocks of classical and modern control systems. In this paper, a three-layer feed-forward neural network (NN) model trained to replicate the behavior of a PID controller is employed to stabilize control systems through a NN feedback controller. A novel bio-inspired weights-and-structure-determination (BIWASD) algorithm, which incorporates a metaheuristic optimization algorithm dubbed beetle antennae search (BAS), is used to train the NN model. More presicely, the BIWASD algorithm identifies the ideal weights and structure of the BIWASD-based NN (BIWASDNN) model utilizing a power sigmoid activation function while handling model fitting and validation. The results of three simulated trials on stabilizing feedback control systems validate and demonstrate the BIWASDNN model’s exceptional learning and prediction capabilities, while achieving similar or better performance than the corresponding PID controller. The BIWASDNN model is compared to three other high-performing NN models, and a MATLAB repository is accessible in public through GitHub to encourage and enhance this work.

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“…In recent decades, neural network has become a focus [18]- [23]. Due to the parallel-distributed processing property, recurrent neural network (RNN) is applied in many fields [24]- [27], especially for time-varying problems [28]- [32].…”

Section: Some Direct Methods Such As Bartels-stewart Methods and Hammentioning

confidence: 99%

Noise-Tolerant Zeroing Neural Network for Solving Non-Stationary Lyapunov Equation

Yan

Xiao

et al. 2019

IEEE Access

Self Cite

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As a crucial means for stability analysis in control systems, the Lyapunov equation is applied in many fields of science and engineering. There are some methods proposed and studied for solving the non-stationary Lyapunov equation, such as the zeroing neural network (ZNN) model. However, a common drawback these methods have is that they rarely tolerate noises. Therefore, given that the existence of various types of noises during computation, a noise-tolerant ZNN (NTZNN) model with anti-noise ability is proposed for solving the non-stationary Lyapunov equation in this paper. For comparison, the conventional ZNN (CZNN) model is also applied to solve the same problem. Furthermore, theoretical analyses are provided to prove the global and exponential convergence performance of the proposed NTZNN model in the absence of noises. On this basis, the anti-noise performance of the proposed NTZNN model is proven. Finally, by adopting the proposed NTZNN model and the CZNN model to solve the non-stationary Lyapunov equation, computer simulations are conducted under the noise-free case and the noisy case, respectively. The simulation results indicate that the proposed NTZNN model is practicable for solving the non-stationary Lyapunov equation and superior to the CZNN model at the existence of noises. INDEX TERMS Non-stationary Lyapunov equation, noise-tolerant zeroing neural network (NTZNN), conventional zeroing neural network (CZNN), global and exponential convergence.

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Weight and Structure Determination Neural Network Aided With Double Pseudoinversion for Diagnosis of Flat Foot

Cited by 30 publications

References 44 publications

Influence of lower leg myofascial kinematic chains on flat feet development of children 7-14 years old

Influence of lower leg myofascial kinematic chains on flat feet development of children 7-14 years old

Feedback Control Systems Stabilization Using a Bio-inspired Neural Network

Noise-Tolerant Zeroing Neural Network for Solving Non-Stationary Lyapunov Equation

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