From Bidirectional Associative Memory to a noise-tolerant, robust Protein Processor Associative Memory

AbstractProtein Processor Associative Memory (PPAM) is a novel architecture for learning associations incrementally and online and performing fast, reliable, scalable hetero-associative recall. This paper presents a comparison of the PPAM with the Bidirectional Associative Memory (BAM), both with Kosko's original training algorithm and also with the more popular Pseudo-Relaxation Learning Algorithm for BAM (PRLAB). It also compares the PPAM with a more recent associative memory architecture called SOIAM. Results of training for object-avoidance are presented from simulations using player/stage and are verified by actual implementations on the E-Puck mobile robot. Finally, we show how the PPAM is capable of achieving an increase in performance without using the typical weighted-sum arithmetic operations or indeed any arithmetic operations

ROBOT MÓVIL MECX1 PARA LA DETECCIÓN DE PERSONAS EMPLEANDO MEMORIAS ASOCIATIVAS ALFA-BETA

ResumenEn presente trabajo de investigación propone emplear a las Memorias Asociativas Alpha-Beta (AMαβ) en la detección automática del cuerpo humano a partir de imágenes RGB-3D capturadas por el robot MECX1, las AMαβ son entrenadas con vectores característicos extraídos de dos tipos de imágenes, las imágenes positivas contienen personas bajo diferentes poses, distancias e iluminación, mientras que las imágenes negativas contienen objetos que el robot puede encontrar en su entorno de navegación. El rendimiento de las AMαβ es evaluado en dos pruebas, en la primera se determina la capacidad para recordar los vectores previamente aprendidos, los resultados muestran que la memoria fue capaz de recordar al 100% las formas de cuerpos humanos, así como de los  objetos con los que fue entrenada, en la segunda prueba se evalúa su capacidad para clasificar vectores que no aprendió anteriormente, obteniéndose una tasa de precisión promedio de 95.1%. Para la validación de los resultados y separación de los conjuntos de entrenamiento y prueba se empleó el método de K-fold-cross-validation.Palabra(s) Clave: cuerpo humano, detección, forma humana, memorias asociativas, reconocimiento. MECX1 MOBILE ROBOT FOR THE DETECTION OF PEOPLE USING ASSOCIATIVE MEMORIES ALPHA-BETA AbstractIn the present work, we propose to use Alpha-Beta Associative Memories (AMαβ) in the automatic detection of the human body from RGB-3D images captured by the robot MECX1, the AMαβ are trained with characteristic vectors extracted from two types of images, positive images contain people under different poses, distances and illumination, while negative images contain objects that the robot can find in its navigation environment. The performance of the AMαβ is evaluated in two tests, the first one determines the ability to remember previously learned vectors, the results show that memory was able to remember 100% human body forms as well as objects with the ones that were trained, in the second test we evaluated the memory capacity to classify vectors that were not previously learned, obtaining an average accuracy rate of 95.1%, K-fold-cross-validation method was used for the validation of the results and separation of the training and test sets.Keywords: associative memories, detection, human body, human shape

Alpha-Beta Bidirectional Associative Memories

Abstract: Most models of Bidirectional associative memories intend to achieve that all trained pattern correspond to stable states; however, this has not been possible. Also, none of the former models has been able to recall all the trained patterns. In this work we introduce a new model of bidirectional associative memory which is not iterative and has no stability problems. It is based on the Alpha-Beta associative memories. This model allows perfect recall of all trained patterns, with no ambiguity and no conditions. An example of fingerprint recognition is presented. Keywords: Bidirectional associative memories, Alpha-Beta associative memories, perfect recall

A New Model of BAM: Alpha-Beta Bidirectional Associative Memories

Abstract — Most models of Bidirectional associative memories intend to achieve that all trained pattern correspond to stable states; however, this has not been possible. Also, none of the former models has been able to recall all the trained patterns. In this work we introduce a new model of bidirectional associative memory which is not iterative and has no stability problems. It is based on the Alpha-Beta associative memories. This model allows, besides correct recall of noisy patterns, perfect recall of all trained patterns, with no ambiguity and no conditions. An example of fingerprint recognition is presented. Index Terms — Bidirectional associative memories, Alpha-Beta associative memories, perfect recall. I

Associative Models for Storing and Retrieving Concept Lattices

Alpha-beta bidirectional associative memories are implemented for storing concept lattices. We use Lindig's algorithm to construct a concept lattice of a particular context; this structure is stored into an associative memory just as a human being does, namely, associating patterns. Bidirectionality and perfect recall of Alpha-Beta associative model make it a great tool to store a concept lattice. In the learning phase, objects and attributes obtained from Lindig's algorithm are associated by Alpha-Beta bidirectional associative memory; in this phase the data is stored. In the recalling phase, the associative model allows to retrieve objects from attributes or vice versa. Our model assures the recalling of every learnt concept