Identifikasi Tingkat Manis Buah Belimbing Berdasarkan Citra Red Green Blue Menggunakan Fuzzy Neural Network

  Fuzzy Neural Network (FNN) has a capability to classify a pattern within two different classes which a classical Neural Network (NN) is failed to do so. The fuzzy pattern classification use membership degree on output of neuron as learning target. This research aim is to develop an artificial intelligence system model for non-destructive classification of starfruit using Fuzzy Neural Network. The input parameter is the estimator parameter of starfruit sweet level of red, green and blue index color obtained from image processing. The best result of starfruit sweet level identification using FNN with three classification class target (sour, medium and sweet) is achieved with 25 neurons in hidden layer and 14th epoch with 100% accuracy.   Keyword : classification, fuzzy neural network, starfruit, non-destructive grading, pattern recognition.   &nbsp

Neuro-Fuzzy System based Handwritten Marathi Numerals Recognition System

Character Recognition is one of the important tasks in Pattern Recognition. The complexity of the character recognition problem depends on the character set to be recognized. In this paper it is developed 0ff-line strategies for the isolated handwritten Marathi numerical (0 to ९) with Neuro fuzzy logic has been provided. The neural fuzzy system is considered for soft computing.  This method improves the character recognition method. Neuro Fuzzy System is integration of Neural Network and Fuzzy logic. In that we are using neural fuzzy system for classification

Fuzzy Clustering Neural Networks for Real-Time Odor Recognition System

The aim of this study is to develop a novel fuzzy clustering neural network (FCNN) algorithm as pattern classifiers for real-time odor recognition system. In this type of FCNN, the input neurons activations are derived through fuzzy c mean clustering of the input data, so that the neural system could deal with the statistics of the measurement error directly. Then the performance of FCNN network is compared with the other network which is well-known algorithm, named multilayer perceptron (MLP), for the same odor recognition system. Experimental results show that both FCNN and MLP provided high recognition probability in determining various learn categories of odors, however, the FCNN neural system has better ability to recognize odors more than the MLP network

Face Recognition Based on Intelligent System

Face recognition is one of the most attractive and challenging topics in the fields of pattern recognition used in many biometric security applications. In this research, we use Two Dimensional Discrete Wavelet Transform (2D-DWT) to decompose face image for extracting features and Principle Component Analysis (PCA) for dimensional reduction of these features. For classification, we use ANFIS technique which is a combination between Fuzzy Inference System and Neural Network, Furthermore, we implemented Back Propagation Neural Network for the extracted features to compare its efficiency with ANFIS. Implementing proposed work get a best recognition rate and less computation where the recognition rate increases until it reaches maximum value (96%)

Klasifikasi Buah Belimbing Manis dan Tidak Manis Berdasarkan Citra Red Green Blue Menggunakan Fuzzy Neural Network

BSTRACTClassical classification problems that can not be solved using the NN can be done using the FNN. Thedifference lies in the use of learning targets, which uses a degree of membership in the output. This studyaims to create a classification of star fruit to sweet and not sweet categories with non destructive methodusing fuzzy neural network. Red green and blue components of the image of the star fruit is used as an inputparameter. FNN 3-15-2 accuration obtained is 88.89% by using 15 neurons in the hidden layer, MSE9.13e-09 at epoch 16th. Keyword : classification, fuzzy neural network, starfruit, non-destructive grading, pattern recognition

ART-EMAP: A Neural Network Architecture for Object Recognition by Evidence Accumulation

A new neural network architecture is introduced for the recognition of pattern classes after supervised and unsupervised learning. Applications include spatio-temporal image understanding and prediction and 3-D object recognition from a series of ambiguous 2-D views. The architecture, called ART-EMAP, achieves a synthesis of adaptive resonance theory (ART) and spatial and temporal evidence integration for dynamic predictive mapping (EMAP). ART-EMAP extends the capabilities of fuzzy ARTMAP in four incremental stages. Stage 1 introduces distributed pattern representation at a view category field. Stage 2 adds a decision criterion to the mapping between view and object categories, delaying identification of ambiguous objects when faced with a low confidence prediction. Stage 3 augments the system with a field where evidence accumulates in medium-term memory (MTM). Stage 4 adds an unsupervised learning process to fine-tune performance after the limited initial period of supervised network training. Each ART-EMAP stage is illustrated with a benchmark simulation example, using both noisy and noise-free data. A concluding set of simulations demonstrate ART-EMAP performance on a difficult 3-D object recognition problem.Advanced Research Projects Agency (ONR N00014-92-J-4015); National Science Foundation (IRI-90-00530); Office of Naval Research (N00014-91-J-4100); Air Force Office of Scientific Research (90-0083

IMPLEMENTATION OF ARTIFICIAL NEURAL NETWORK IN NANO SCALE ENVIRONMENT

Facial recognition systems are computer-based security systems that are able to automatically detect and identify human faces. Facial recognition has gained increasing interest in the recent decade. Over the years there have been several techniques being developed to achieve high success rate of accuracy in the identification and verification of individuals for authentication in security systems. This project experiments the concept of neural network for facial recognition that can differentiate and recognize face of image. This face recognition system begins with image pre-processing and then the output image is trained using Fuzzy c-means clustering (FCM) algorithm. FCM network learns by training the inputs, calculating the error between the real output and target output, and propagates back the error to the network to modify the weights until the desired output is obtained. After training the network, the recognition system is tested to ensure that the system can recognize the pattern of each face image. The purpose of this project is to recognize face of image for the recognition analysis using Neural Network and capture the brainwaves of the emotion recognition. This project is mainly concern with facial recognition systems using purely image processing technique

Individual And Ensemble Pattern Classification Models Using Enhanced Fuzzy Min-Max Neural Networks

Pattern classification is one of the major components for the design and development of a computerized pattern recognition system. Focused on computational intelligence models, this thesis describes in-depth investigations on two possible directions to design robust and flexible pattern classification models with high performance. Firstly is by enhancing the learning algorithm of a neural-fuzzy network; and secondly by devising an ensemble model to combine the predictions from multiple neural-fuzzy networks using an agent-based framework. Owing to a number of salient features which include the ability of learning incrementally and establishing nonlinear decision boundary with hyperboxes, the Fuzzy Min-Max (FMM) network is selected as the backbone for designing useful and usable pattern classification models in this research. Two enhanced FMM variants, i.e. EFMM and EFMM2, are proposed to address a number of limitations in the original FMM learning algorithm. In EFMM, three heuristic rules are introduced to improve the hyperbox expansion, overlap test, and contraction processes. The network complexity and noise tolerance issues are undertaken in EFMM2. In addition, an agent-based framework is capitalized as a robust ensemble model to house multiple EFMM-based networks. A useful trust measurement method known as Certified Belief in Strength (CBS) is developed and incorporated into the ensemble model for exploiting the predictive performances of different EFMM-based networks

Design of Neural Network System to Communicate a Blind Person with a Computer Using a Braille

Artificial intelligence systems are widely used nowadays in solving different problems facing mankind. Artificial intelligence systems were developed based on how human brains can think and function; this distinguished character gives these systems the wide range of applications as compared to conventional systems. Among the artificial intelligent systems involves artificial neural intelligent, fuzzy systems and neural fuzzy systems. Blind person interface to computer is among the problem which the world is facing. In this paper the Artificial Neural Network (ANN) is used in designing the Braille system which is capable of enabling the interface between a blind person and the computer. The Multilayer perceptron (MLP) with four layers and nineteen neural is used for the implementation of pattern recognition of the Braille. The pattern of the Braille is used as the inputs to the MLP whereby through MATLAB developed program the patterns training are achieved. The developed system is capable of enabling the blind person to typing letters, numbers and to enter different commands to the computer.

การประยุกต์ใช้ระบบควบคุมการเคลื่อนที่อัตโนมัติของรถไถสำหรับ ควบคุมการติดตามเส้น

บทคัดย่อ บทความวิชาการนี้เป็นการรวบรวมข้อมูลอัลกอลิทึมของระบบควบคุมการเคลื่อนที่อัตโนมัติของรถไถที่ใช้ในการควบคุมการติดตามเส้น โดยในเบื้องต้นต้องวิเคราะห์ลักษณะของรถไถว่ามีรูปแบบการเคลื่อนที่อย่างไร เช่น มีระบบขับเคลื่อน และการบังคับเลี้ยวเช่นใด เพื่อทำการสร้างสมการทางพลศาสตร์สำหรับรถไถในลำดับต่อไป  และเนื่องจากการควบคุมรถไถให้วิ่งตามเส้นได้นั้นต้องมีปัจจัยสำคัญอื่น ๆ เข้ามาเกี่ยวข้อง เช่น กระบวนการสร้างเส้นทางการเคลื่อนที่ (Part Trajectory) ซึ่งเปรียบเสมือนการสร้างถนนในรูปแบบต่างๆ เพื่อให้รถไถเคลื่อนไปตามเส้นทางที่กำหนด  ส่วนอีกปัจจัยหนึ่งที่ช่วยให้รถไถเคลื่อนที่ไปได้อย่างมีประสิทธิภาพคือ ระบบควบคุมซึ่งทำหน้าที่เสมือนผู้ขับขี่รถไถ  โดยระบบหนึ่งที่นำมาใช้ในการควบคุมการเคลื่อนของรถไถคือ ระบบฟัซซี ลอจิก (Fuzzy Logic Control) สำหรับคุณสมบัติที่ดีของระบบนี้คือ การมีเหตุผลเชิงตรรกะซึ่งสอดคล้องกับตรรกะทางความคิดของมนุษย์ โดยโครงสร้างของระบบฟัซซีสามารถทำความเข้าใจสถานการณ์ด้วยการตีความในรูป If-Then และสามารถตัดสินใจในสถานการที่คลุมเครือได้ มิใช่พิจารณาว่าผิดหรือถูกเพียงสองสถานะเท่านั้น  อย่างไรก็ตาม เนื่องจากระบบฟัซซี ลอจิก ไม่มีกระบวนการเรียนรู้ในการปรับแต่งโครงสร้างของกฎและตัวแปรต่าง ๆ ในตัวระบบได้เอง  จึงมีการนำระบบควบคุมอีกชนิดหนึ่งได้แก่ โครงข่ายประสาทเทียม (Neural Network) ซึ่งมีความสามารถในการเรียนรู้ด้วยการจดจำรูปแบบ (Pattern Recognition)  และการอุปมานความรู้เช่นเดียวกับความสามารถที่มีในสมองมนุษย์ โดยการนำระบบนี้มาผสมผสานกับระบบควบคุมแบบฟัซซี ลอจิก ซึ่งเรียกว่าระบบ อนุมานนิวโรฟัซซี (Neuro-Fuzzy System) และเป็นระบบที่นำมาใช้เพื่อเพิ่มประสิทธิภาพในการควบคุมการเคลื่อนที่ของรถไถให้ดียิ่งขึ้น คำสำคัญ: ฟัซซีลอจิก โครงข่ายประสาทเทียม การติดตามเส้น สมการพลศาสตร์ รถไถ  ABSTRACT This article discusses the algorithm of autonomous steering with path-tracking system of tractor. And therefore, the characteristics of steering is analyzed in order to design dynamic equation.   Regarding tractor’s path tracking control, there are various significant factors related such as the creation process of parth trajectory which is similar to variety forms of road for tractor to reach the regulated path.  Another factor for effective autonomous steering is controlling system which is similar to tractor driver.  One of the system applied for steering control is Fuzzy Logic System.  Advantage characteristics of the system is its logical reasoning which is consistent with human’s logical decision.  The system possesses an ability to understand a circumstance by if-then translation and to decide among ambiguous situation which is not only yes or no consideration.   However, learning process of Fuzzy Logic System cannot modify the structure of rules and variables by itself.  Therefore, another controlling system called Neural Network is applied.  Neural Network possesses an ability to learn by pattern recognition and by inductive thinking in the same way as human does.    Fuzzy Logic System and Neural Network are fused to be Neuro-Fuzzy System which is applied for a better effective autonomous steering of tractor studied.Keyword: Fuzzy logic, Neural network, Path tracking, Dynamic equation, Tracto