Levenberg Marquardt Algorithm Case Study

Abstract— Leaves have a noteworthy part in plant species furthermore in nature. There are sure leaves which have therapeutic qualities. Recognizable proof of carbon copy departs is turning into a noteworthy goal for investigative purposes. Keeping in mind the end goal to beat that, PC vision system is used, which incorporates picture handling calculation. Manual distinguishing proof for the leaves sometimes prompts more imperative qualification because of twin. Consequently, validation of leaves is greatly required for restorative purposes. The main answer for this issue is Image Processing. The components of a leaf are removed utilizing GLCM Technique and a nearest match is taken to distinguish which class it fits in with. In this Paper, the execution examination of counterfeit neural system calculation and Levenberg Marquardt Algorithm is compared. In the Levenberg Marquardt calculation, the damping parameter assumes an essential part which accomplishes for the more noteworthy joining rate.

Index Terms—Artificial Neural Network, GLCM Technique,Identification of leaves,Levenberg-Marquardt Algorithm,Probabilistic Neural Network.

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1 Introduction

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EAVESconstitute the most basic part of a plant. Leaf recognizable

RBF is a ringer shape work that scales the variable nonlinearly. PNN is received for various central focuses. Its arrangement speed is customarily speedier than a BP system. PNN can approach a base ideal examination under effectively met conditions. The most critical point of preference of PNN is that, preparation is straightforward and momentary [10]. Weights are not arranged, yet rather doled out. Existing weights will never be rotated yet just new vectors are embedded into weight grids when prepared. So it can be utilized as a part of