Adaptive Resonance Theory

Air Force Office of Scientific Research (F49620-92-J-0225); National Science Foundation (IRI-90-00530); Office of Naval Research (N00014-91-J-4100

Adaptive Resonance Theory

SyNAPSE program of the Defense Advanced Projects Research Agency (Hewlett-Packard Company, subcontract under DARPA prime contract HR0011-09-3-0001, and HRL Laboratories LLC, subcontract #801881-BS under DARPA prime contract HR0011-09-C-0001); CELEST, an NSF Science of Learning Center (SBE-0354378

Adaptive Resonance Theory

Defense Advanced Research Projects Agency and the Office of Naval Research (N00014-95-l-0409); National Science Foundation (IRI-97-20333); Office of Naval Research (N00014-95-l-0657

ALGORITMA PENGENALAN SIDIK JARI MENGGUNAKAN ADAPTIVE RESONANCE THEORY DAN FILTER GABOR “FINGERPRINT RECOGNITION ALGORITHM USING ADAPTIVE RESONANCE THEORY AND GABOR FILTER”

ABSTRAKSI: Banyaknya pemalsuan identitas dalam lingkungan kita maupun dalam dunia maya, memaksa munculnya teknologi identifikasi yang lebih handal. Teknologi identifikasi biometrik seperti penggunaan sidik jari diharapkan mampu menjawab tantangan tersebut. Algoritma yang digunakan untuk implementasi sistem identifikasi sidik jari ini menggunakan filter Gabor 2D, serta penggunaan model jaringan syaraf tiruan Adaptive Resonance Theory untuk teknik identifikasi. Filter Gabor 2D digunakan untuk memperbaiki kualitas citra sidik jari hasil akusisi, serta untuk mengambil ciri makro dan mikro yang terdapat dalam citra sidik jari, sehingga diperoleh jumlah ciri yang tetap untuk setiap sidik jari. Adapative Resonance Theory 2 dipilih untuk teknik identifikasi karena kemampuannya untuk menerima informasi baru tanpa melupakan informasi sebelumnya, sama seperti cara kerja otak manusia. Pengujian identifikasi algoritma ini dibandingkan dengan metoda euclidean distance. Dari hasil pengujian diperoleh bahwa penggunaan blok enhancement dalam sistem dapat meningkatkan akurasi sistem. Hasil akurasi terbaik adalah 92,67% dengan menggunakan ART2 dan 96,67% dengan penggunaan euclidean distance.Kata Kunci : Biometrik, Adaptive Resonance Theory, Filter Gabor 2D, feature extractor, identifikasi, sidik jari.ABSTRACT: The unauthorized using of identity is a common problem in our society and cyber world, it insists the advent of new identification system. Secure identification technology such as biometric identification technology using fingerprint identification is one solution for that problem. The algorithm of fingerprint recognition system is implemented by 2D Gabor filter and artificial neural network Adaptive Resonance Theory as the recognition part. The 2D Gabor filter is used to enhance the image from acquisition device and to capture both macro and micro features from the fingerprint image as a fixed length code. ART neural network is chosen because of its ability to receive new information without losing the previous info, similar to human brain work The algorithm has been tested and compared with euclidean distance method. The result show that the enhancement block on the system can improve identification accuracy. The best recognition result is 92,67% for ART2 method and 96,67% for euclidean distance method.Keyword: Biometric, Adaptive Resonance Theory, 2D Gabor Filter, feature extractor, identification ,fingerprint

Adaptive Resonance: An Emerging Neural Theory of Cognition

Adaptive resonance is a theory of cognitive information processing which has been realized as a family of neural network models. In recent years, these models have evolved to incorporate new capabilities in the cognitive, neural, computational, and technological domains. Minimal models provide a conceptual framework, for formulating questions about the nature of cognition; an architectural framework, for mapping cognitive functions to cortical regions; a semantic framework, for precisely defining terms; and a computational framework, for testing hypotheses. These systems are here exemplified by the distributed ART (dART) model, which generalizes localist ART systems to allow arbitrarily distributed code representations, while retaining basic capabilities such as stable fast learning and scalability. Since each component is placed in the context of a unified real-time system, analysis can move from the level of neural processes, including learning laws and rules of synaptic transmission, to cognitive processes, including attention and consciousness. Local design is driven by global functional constraints, with each network synthesizing a dynamic balance of opposing tendencies. The self-contained working ART and dART models can also be transferred to technology, in areas that include remote sensing, sensor fusion, and content-addressable information retrieval from large databases.Office of Naval Research and the defense Advanced Research Projects Agency (N00014-95-1-0409, N00014-1-95-0657); National Institutes of Health (20-316-4304-5

Peramalan Nilai Tukar Valuta Asing Menggunakan Fuzzy Adaptive Resonance Theory (Fuzzy ART) Forecasting Foreign Exchange Rates Using Fuzzy Adaptive Resonance Theory (Fuzzy ART)

ABSTRAKSI: Nilai tukar valuta merupakan nilai yang sangat krusial untuk diolah dan diprediksi karena merupakan nilai yang berbanding dengan kerugian dan keuntungan dari pihak penanam modal. Nilai tukar valuta asing bisa mendatangkan keuntungan maupun kerugian dalam skala besar bagi siapa saja yang melakukan transaksi terhadap nilai tukar valas tersebut tergantung benar tidaknya hasil prediksi dan besar kecilnya modal yang ditanam, karena itulah diperlukan suatu analisis yang baik terhadap pergerakan data, dalam hal ini data valuta asing. Banyak metode yang telah dikembangkan untuk mendapatkan hasil peramalan yang akurat terutama dengan menggunakan metode jaringan syaraf tiruan (JST). Dalam tugas akhir ini menggunakan metode JST dengan pembelajaran tanpa guru (unsupervised) yaitu Fuzzy ART. Adapun sistem ini sendiri memiliki berbagai parameter yang berperan dalam menghasilkan evaluasi prediksi yang baik.Kata Kunci : prediksi , JST, Fuzzy ART, unsupervised, evaluasi.ABSTRACT: Forecasting exchange rates is an important financial problem that is receiving increasing attention especially because of its difficulty and practical applications. Many financial institutions evaluate prediction algorithms using the percentage of times that the algorithm predicts the right trend from today until some time in the future. And research efforts on ANNs for forecasting exchange rates are also considerable. In this paper, the writer attempt to provide a survey of research in this area. Several design factors significantly impact the accuracy of neural network forecasts. These factors include the selection of input variables, preparing data, and network architecture. There is no consensus about the factorsKeyword: forecasting, financial, ANN, accuracy

PENGENALAN CITRA WAJAH MANUSIA MENGGUNAKAN PRINCIPAL COMPONENT ANALYSIS DAN ADAPTIVE RESONANCE THEORY (ART) “FACE RECOGNITION USING PRINCIPAL COMPONENT ANALYSIS AND ADAPTIVE RESONANCE THEORY”

ABSTRAKSI: Pengenalan citra wajah merupakan proses untuk mengenali dan menentukan seseorang. Teknologi pengenalan citra wajah termasuk di dalam biometrik yang menggunakan karekteristik manusia. Saat ini pengenalan wajah dapat digunakan dalam berbagai hal, diantaranya untuk keamanan, pengenalan identitas pegawai, meningkatkan efisiensi dan efektifitas berbagai kegiatan, yaitu dengan mengurangi pemakaian kartu identitas dan kata sandi.Sistem pengenalan yang diimplementasikan ini menggunakan model Jaringan Saraf Tiruan Adaptive Resonance Theory (ART). JST ART ini memiliki kemampuan untuk menerima informasi baru tanpa melupakan informasi sebelumnya, sama seperti cara kerja otak manusia. Untuk dapat mengidentifikasi citra wajah, jaringan saraf tiruan memerlukan preprocessing dan feature extracting terlebih dulu. Proses ekstraksi ciri dengan Principal Component Analysis (PCA) bertujuan untuk mendapatkan informasi ciri yang penting dari citra wajah dan nilainya diambil sebagai masukan untuk jaringan saraf tiruan. Pelatihan JST dilakukan untuk mendapatkan klasifikasi yang tepat dari masukan data latih citra wajah asli. Citra wajah dapat dikenali jika citra wajah tersebut masuk dalam salah satu kelas yang terbentuk dari proses pelatihan.Dari hasil pengujian diperoleh tingkat keakuratan sistem pengenalan citra wajah dengan klasifikasi terbaik adalah sekitar 96% untuk bisa mengenali citra wajah asli, dan sekitar 80% – 100% untuk menolak citra wajah palsu.Kata Kunci : Biometrik, JST Adaptive Resonance Theory, preprocessing,ABSTRACT: Face recognition is a process to recognize and decide someone by his face. Face recognition technology include on biometric which use natural human behavior characteristics. Nowadays, face recognition can be use for many things for example: security, employee identity recognition, and crime subject identification. Face recognition also can be use to make many things more efficient and effective by reduce the using of password and identity card.Identification system implemented using Adaptive Resonance Theory (ART) neural network models. ART neural network is capable to receive new information without forgetting the previous information, like as the way of human brain work. To be able to recognize face image, neural network need preprocessing and feature extracting. Extraction process with Principal Component Analysis to get the important feature information from face image and its value is taken as input to neural network. Learning of neural network is conducted to get the correct classification from data training a genuine face image. The face image can be recognized if it set in one of the class from training process.From the testing result is obtained by level accuracy of face recognition system with the best classification is 96% for can be recognized a genuine face image, and around 80% – 100% for reject a forged face image.Keyword: Biometric, Adaptive Resonance Theory, neural network