Advances on bidirectional intelligence are overviewed along three threads,with extensions and new perspectives.The first thread is about bidirectional learning architecture,exploring five dualities that enable Lmser s...Advances on bidirectional intelligence are overviewed along three threads,with extensions and new perspectives.The first thread is about bidirectional learning architecture,exploring five dualities that enable Lmser six cognitive functions and provide new perspectives on which a lot of extensions and particularlly flexible Lmser are proposed.Interestingly,either or two of these dualities actually takes an important role in recent models such as U-net,ResNet,and Dense Net.The second thread is about bidirectional learning principles unified by best yIng-yAng(IA)harmony in BYY system.After getting insights on deep bidirectional learning from a bird-viewing on existing typical learning principles from one or both of the inward and outward directions,maximum likelihood,variational principle,and several other learning principles are summarised as exemplars of the BYY learning,with new perspectives on advanced topics.The third thread further proceeds to deep bidirectional intelligence,driven by long term dynamics(LTD)for parameter learning and short term dynamics(STD)for image thinking and rational thinking in harmony.Image thinking deals with information flow of continuously valued arrays and especially image sequence,as if thinking was displayed in the real world,exemplified by the flow from inward encoding/cognition to outward reconstruction/transformation performed in Lmser learning and BYY learning.In contrast,rational thinking handles symbolic strings or discretely valued vectors,performing uncertainty reasoning and problem solving.In particular,a general thesis is proposed for bidirectional intelligence,featured by BYY intelligence potential theory(BYY-IPT)and nine essential dualities in architecture,fundamentals,and implementation,respectively.Then,problems of combinatorial solving and uncertainty reasoning are investigated from this BYY IPT perspective.First,variants and extensions are suggested for AlphaGoZero like searching tasks,such as traveling salesman problem(TSP)and attributed graph matching(AGM)that are turned into Go like problems with help of a feature enrichment technique.Second,reasoning activities are summarized under guidance of BYY IPT from the aspects of constraint satisfaction,uncertainty propagation,and path or tree searching.Particularly,causal potential theory is proposed for discovering causal direction,with two roads developed for its implementation.展开更多
As a supplementary of [Xu L. Front. Electr. Electron. Eng. China, 2010, 5（3）： 281-328], this paper outlines current status of efforts made on Bayesian Ying- Yang （BYY） harmony learning, plus gene analysis appli- ...As a supplementary of [Xu L. Front. Electr. Electron. Eng. China, 2010, 5（3）： 281-328], this paper outlines current status of efforts made on Bayesian Ying- Yang （BYY） harmony learning, plus gene analysis appli- cations. At the beginning, a bird＇s-eye view is provided via Gaussian mixture in comparison with typical learn- ing algorithms and model selection criteria. Particularly, semi-supervised learning is covered simply via choosing a scalar parameter. Then, essential topics and demand- ing issues about BYY system design and BYY harmony learning are systematically outlined, with a modern per- spective on Yin-Yang viewpoint discussed, another Yang factorization addressed, and coordinations across and within Ying-Yang summarized. The BYY system acts as a unified framework to accommodate unsupervised, su- pervised, and semi-supervised learning all in one formu- lation, while the best harmony learning provides novelty and strength to automatic model selection. Also, mathe- matical formulation of harmony functional has been ad- dressed as a unified scheme for measuring the proximity to be considered in a BYY system, and used as the best choice among others. Moreover, efforts are made on a number of learning tasks, including a mode-switching factor analysis proposed as a semi-blind learning frame- work for several types of independent factor analysis, a hidden Markov model （HMM） gated temporal fac- tor analysis suggested for modeling piecewise stationary temporal dependence, and a two-level hierarchical Gaus- sian mixture extended to cover semi-supervised learning, as well as a manifold learning modified to facilitate au- tomatic model selection. Finally, studies are applied to the problems of gene analysis, such as genome-wide asso- ciation, exome sequencing analysis, and gene transcrip- tional regulation.展开更多
In this paper, a methodology, Self-Developing and Self-Adaptive Fuzzy Neural Networks using Type-2 Fuzzy Bayesian Ying-Yang Learning （SDSA-FNN-T2FBYYL） algorithm and multi-objective optimization is proposed. The fea...In this paper, a methodology, Self-Developing and Self-Adaptive Fuzzy Neural Networks using Type-2 Fuzzy Bayesian Ying-Yang Learning （SDSA-FNN-T2FBYYL） algorithm and multi-objective optimization is proposed. The features of this methodology are as follows： （1） A Bayesian Ying-Yang Learning （BYYL） algorithm is used to construct a compact but high-performance system automatically. （2） A novel multi-objective T2FBYYL is presented that integrates the T2 fuzzy theory with BYYL to automatically construct its best structure and better tackle various data uncertainty problems simultaneously. （3） The weighted sum multi-objective optimization technique with combinations of different weightings is implemented to achieve the best trade-off among multiple objectives in the T2FBYYL. The proposed methods are applied to electric load forecast using a real operational dataset collected from Macao electric utility. The test results reveal that the proposed method is superior to other existing relevant techniques.展开更多
基金supported by the Zhi-Yuan Chair Professorship Start-up Grant (WF220103010) from Shanghai Jiao Tong University
文摘Advances on bidirectional intelligence are overviewed along three threads,with extensions and new perspectives.The first thread is about bidirectional learning architecture,exploring five dualities that enable Lmser six cognitive functions and provide new perspectives on which a lot of extensions and particularlly flexible Lmser are proposed.Interestingly,either or two of these dualities actually takes an important role in recent models such as U-net,ResNet,and Dense Net.The second thread is about bidirectional learning principles unified by best yIng-yAng(IA)harmony in BYY system.After getting insights on deep bidirectional learning from a bird-viewing on existing typical learning principles from one or both of the inward and outward directions,maximum likelihood,variational principle,and several other learning principles are summarised as exemplars of the BYY learning,with new perspectives on advanced topics.The third thread further proceeds to deep bidirectional intelligence,driven by long term dynamics(LTD)for parameter learning and short term dynamics(STD)for image thinking and rational thinking in harmony.Image thinking deals with information flow of continuously valued arrays and especially image sequence,as if thinking was displayed in the real world,exemplified by the flow from inward encoding/cognition to outward reconstruction/transformation performed in Lmser learning and BYY learning.In contrast,rational thinking handles symbolic strings or discretely valued vectors,performing uncertainty reasoning and problem solving.In particular,a general thesis is proposed for bidirectional intelligence,featured by BYY intelligence potential theory(BYY-IPT)and nine essential dualities in architecture,fundamentals,and implementation,respectively.Then,problems of combinatorial solving and uncertainty reasoning are investigated from this BYY IPT perspective.First,variants and extensions are suggested for AlphaGoZero like searching tasks,such as traveling salesman problem(TSP)and attributed graph matching(AGM)that are turned into Go like problems with help of a feature enrichment technique.Second,reasoning activities are summarized under guidance of BYY IPT from the aspects of constraint satisfaction,uncertainty propagation,and path or tree searching.Particularly,causal potential theory is proposed for discovering causal direction,with two roads developed for its implementation.
文摘As a supplementary of [Xu L. Front. Electr. Electron. Eng. China, 2010, 5（3）： 281-328], this paper outlines current status of efforts made on Bayesian Ying- Yang （BYY） harmony learning, plus gene analysis appli- cations. At the beginning, a bird＇s-eye view is provided via Gaussian mixture in comparison with typical learn- ing algorithms and model selection criteria. Particularly, semi-supervised learning is covered simply via choosing a scalar parameter. Then, essential topics and demand- ing issues about BYY system design and BYY harmony learning are systematically outlined, with a modern per- spective on Yin-Yang viewpoint discussed, another Yang factorization addressed, and coordinations across and within Ying-Yang summarized. The BYY system acts as a unified framework to accommodate unsupervised, su- pervised, and semi-supervised learning all in one formu- lation, while the best harmony learning provides novelty and strength to automatic model selection. Also, mathe- matical formulation of harmony functional has been ad- dressed as a unified scheme for measuring the proximity to be considered in a BYY system, and used as the best choice among others. Moreover, efforts are made on a number of learning tasks, including a mode-switching factor analysis proposed as a semi-blind learning frame- work for several types of independent factor analysis, a hidden Markov model （HMM） gated temporal fac- tor analysis suggested for modeling piecewise stationary temporal dependence, and a two-level hierarchical Gaus- sian mixture extended to cover semi-supervised learning, as well as a manifold learning modified to facilitate au- tomatic model selection. Finally, studies are applied to the problems of gene analysis, such as genome-wide asso- ciation, exome sequencing analysis, and gene transcrip- tional regulation.
基金supported by the Research Committee of University of Macao with Grant No. MYRG2014-00060FSTthe Science and Technology Development Fund (FDCT) of Macao S.A.R with Grant No. 016/2012/A1
文摘In this paper, a methodology, Self-Developing and Self-Adaptive Fuzzy Neural Networks using Type-2 Fuzzy Bayesian Ying-Yang Learning （SDSA-FNN-T2FBYYL） algorithm and multi-objective optimization is proposed. The features of this methodology are as follows： （1） A Bayesian Ying-Yang Learning （BYYL） algorithm is used to construct a compact but high-performance system automatically. （2） A novel multi-objective T2FBYYL is presented that integrates the T2 fuzzy theory with BYYL to automatically construct its best structure and better tackle various data uncertainty problems simultaneously. （3） The weighted sum multi-objective optimization technique with combinations of different weightings is implemented to achieve the best trade-off among multiple objectives in the T2FBYYL. The proposed methods are applied to electric load forecast using a real operational dataset collected from Macao electric utility. The test results reveal that the proposed method is superior to other existing relevant techniques.
