Parameter Optimization in Convolutional Neural Networks Using Gradient Descent

Suspicious mass traffic constantly evolves, making network behaviour tracing and structure more complex. Neural networks yield promising results by considering a sufficient number of processing elements with strong interconnections between them. They offer efficient computational Hopfield neural networks models and optimization constraints used by undergoing a good amount of parallelism to yield optimal results. Artificial neural network (ANN) offers optimal solutions in classifying and clustering the various reels of data, and the results obtained purely depend on identifying a problem. In this research work, the design of optimized applications is presented in an organized manner. In addition, this research work examines theoretical approaches to achieving optimized results using ANN. It mainly focuses on designing rules. The optimizing design approach of neural networks analyzes the internal process of the neural networks. Practices in developing the network are based on the interconnections among the hidden nodes and their learning parameters. The methodology is proven best for nonlinear resource allocation problems with a suitable design and complex issues. The ANN proposed here considers more or less 46k nodes hidden inside 49 million connections employed on full-fledged parallel processors. The proposed ANN offered optimal results in real-world application problems, and the results were obtained using MATLAB.

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“…The conceptual framework for using neural network optimization functions of networks is the energy function given by the formula [33,34].…”

Section: Proposed Methods Of Optimization Techniques Using Neural Net...mentioning

confidence: 99%

Design of ANN Based Non-Linear Network Using Interconnection of Parallel Processor

Singha¹,

Zubair²,

Malibari³

et al. 2023

Computer Systems Science and Engineering

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“…Supplement: The mian difference between this paper and the literature [16] is formula (29), helping the controller to achieve stably error-free tracking. The first control law in [16] referred to in this paper is the modelbased control for the system with known model and parameters.…”

Section: Wheeled Mobile Robots Modelmentioning

confidence: 97%

“…The model-based parameter optimal method proposed in this paper only needs the settling time to be calculatable. Here we take the gradient descent optimal method to design the modelbased parameter optimal strategy for settling time [29]- [31]. With the proposed method, the position and velocity errors can converge to zero.…”

Section: Main Contributionsmentioning

confidence: 99%

“…Cauchy proposed the gradient descent method in 1847. Due to the simple concept, the gradient descent method has become one of the most commonly used parameter minimization methods [29]- [31]. The basic idea of this method is to assume that the gradient vector is in the steepest ascending direction and take a sufficiently small step in the opposite direction, which causes the function value to be lower than the previous value.…”

Section: Model-based Parameter Optimization Controlmentioning

confidence: 99%

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A Model-based Parameter Optimization Control Strategy for Trajectory Tracking with Torque and Velocity Constraints

Luo

Hou

et al. 2021

Preprint

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The wheeled robots trajectory tracking control methods rarely constrain the torque and speed at the same time. In actual application, the torque and speed of the robot cannot exceed the saturation limit of the actuator. This paper develops a model-based trajectory tracking parameter optimization controller with both velocity and torque constraints, using a gradient descent parameter iterative learning strategy to minimize the settling time index of the system. Trajectory tracking time optimization methods usually require a given analytical expression of the system time, while this time optimization method only requires that the settling time is solvable. The MATLAB simulation experiments show that the proposed parameter optimization controller for trajectory tracking can perform velocity and torque constraints while having a relatively good overall rapidity time index. If the resolution of the robot sensor can meet the design requirements, the optimization method can strictly control the system torque maximum to a reasonably small expected value. When the resolution of the robot sensor is limited, this optimization method can restrict the system torque maximum within a reasonable saturation constraint range.

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“…Its presence of the small-world C.N. phenomenon, for example, has been demonstrated in the neural network of the Caenorhabditis elegans worm [17]. Following discovered of similar topological characteristics in human brain networks, such as encephalography recordings [18], cortical connections analyses [19], and the medial reticular development of the brain stem [20].…”

Section: Related Workmentioning

confidence: 99%

Performance Enhancement of Adaptive Neural Networks Based on燣earning燫ate

Zubair¹,

Singha²,

Pathak³

et al. 2023

Computers, Materials &Amp; Continua

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Deep learning is the process of determining parameters that reduce the cost function derived from the dataset. The optimization in neural networks at the time is known as the optimal parameters. To solve optimization, it initialize the parameters during the optimization process. There should be no variation in the cost function parameters at the global minimum. The momentum technique is a parameters optimization approach; however, it has difficulties stopping the parameter when the cost function value fulfills the global minimum (non-stop problem). Moreover, existing approaches use techniques; the learning rate is reduced during the iteration period. These techniques are monotonically reducing at a steady rate over time; our goal is to make the learning rate parameters. We present a method for determining the best parameters that adjust the learning rate in response to the cost function value. As a result, after the cost function has been optimized, the process of the rate Schedule is complete. This approach is shown to ensure convergence to the optimal parameters. This indicates that our strategy minimizes the cost function (or effective learning). The momentum approach is used in the proposed method. To solve the Momentum approach non-stop problem, we use the cost function of the parameter in our proposed method. As a result, this learning technique reduces the quantity of the parameter due to the impact of the cost function parameter. To verify that the learning works to test the strategy, we employed proof of convergence and empirical tests using current methods and the results are obtained using Python.

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Parameter Optimization in Convolutional Neural Networks Using Gradient Descent

Cited by 8 publications

References 6 publications

Design of ANN Based Non-Linear Network Using Interconnection of Parallel Processor

Design of ANN Based Non-Linear Network Using Interconnection of Parallel Processor

A Model-based Parameter Optimization Control Strategy for Trajectory Tracking with Torque and Velocity Constraints

Performance Enhancement of Adaptive Neural Networks Based on燣earning燫ate

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