Autonomous Learning Multimodel Systems From Data Streams

Angelov, Plamen; Gu, Xiaowei; Prı́ncipe, José C.

doi:10.1109/tfuzz.2017.2769039

Cited by 78 publications

(107 citation statements)

References 40 publications

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“…The ALMMo system [12] was recently introduced within the EDA framework [13]- [15]. In this section, the concepts of the 0-order AnYa FRB system and the EDA estimator will be briefly recalled.…”

Section: Basic Conceptsmentioning

confidence: 99%

“…In this paper, we will employ the unimodal density [12] from the EDA framework as the main estimator for disclosing the ensemble properties from the observed data in a fully autonomous way.…”

Section: B Eda Estimatormentioning

confidence: 99%

“…Autonomous Learning Multiple-Model (ALMMo) system [12] was recently introduced in the form of an AnYa FRB system [10], [11] within the Empirical Data Analytics (EDA) [13]- [15] framework to extract shape-free data clouds [10], [11] and autonomously form simple linguistic rules from the empirically observed data. The ALMMo system forms its multi-model structure in an entirely data-driven way without making prior assumptions.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Autonomous learning multi-model classifier of 0-Order (ALMMo-0)

Angelov

2017

2017 Evolving and Adaptive Intelligent Systems (EAIS)

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Section: Basic Conceptsmentioning

confidence: 99%

Section: B Eda Estimatormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Autonomous learning multi-model classifier of 0-Order (ALMMo-0)

Angelov

2017

2017 Evolving and Adaptive Intelligent Systems (EAIS)

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“…As we stressed in the previous paragraph, by "positive" and "negative", we can consider the two PV pairs that are clustered into different data cloud/cluster. For this purpose, we built a fully autonomous AnYa type self-learning fuzzy rule-based zero order classifier [26] per person per day, and learn to classify the individual's perceptions. The actual human-intelligible labels ("positive" and "negative") require cognitive feedback (see, Fig.1) and are part of the second stage of the 2 hierarchical structure/ architecture (see Fig.…”

Section: Experimental Study and Analysismentioning

confidence: 99%

Cybernetics of the Mind: Learning Individual's Perceptions Autonomously

Angelov

Iglesias

et al. 2017

IEEE Syst. Man Cybern. Mag.

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In this paper, we describe an approach to computational modelling and autonomous learning of the perception of sensory inputs by individuals. A hierarchical process of summarization of raw data with heterogeneous nature is proposed. At the lower layer of the hierarchy, the raw data autonomously forms semantically meaningful concepts.Instead of clustering based on the visual or audio similarity, the concepts are being formed at the second layer of the hierarchy based on the observed physiological variables (PVs) like heartbeat, skin conductance, etc. and mapped to the emotional state of the individual. Wearable sensors were used in the experiments. Methodologically, we used the recently introduced Empirical Data Analytics (EDA) computational framework and the data partitioning method within EDA to form the data clouds (cluster-like formations with no pre-defined shape) autonomously and used AnYa type of IF-THEN fuzzy rule-based models to describe the mapping of the observable PVs to the emotional states. Multi-modal typicality distributions, which have properties like pdf and indicate the empirical likelihood distributions of the data, can be further derived without making prior restrictive assumptions traditionally required in statistical approaches. The quality of the classifier is evaluated by a confusion matrix and classification rate/precision. The experimental results are very encouraging: for this extremely complicated problem, we got 74.58% correct classification results without any pre-training.

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“…The homogeneous multiple model was first presented by Takagi and Sugeno with the well-known T-S model, which is used for fuzzy system modeling and control [4,12], and was theorized by Murray-Smith and Johansen with the multiple model approach [3]. The homogeneous multiple model has been widely applied to nonlinear system modeling [13][14][15], nonlinear system control and optimization [16,17], fault detection [18], learning [19,20], etc. However, in some cases the curse of dimensionality, where the number of state variables increases with nonlinear system complexity, is troublesome [13].…”

Section: Introductionmentioning

confidence: 99%

A Control-Performance-Based Partitioning Operating Space Approach in a Heterogeneous Multiple Model

Liu

Yue

2020

Processes

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An operating space partition method with control performance is proposed, where the heterogeneous multiple model is applied to a nonlinear system. Firstly, the heterogeneous multiple model is obtained from a nonlinear system at the given equilibrium points and transformed into a homogeneous multiple model with auxiliary variables. Secondly, an optimal problem where decision variables are composed of control input and boundary conditions of sub-models is formulated with the hybrid model developed from the homogeneous multiple model. The computational implementation of an optimal operating space partition algorithm is presented according to the Hamilton-Jacobi-Bellman equation and numerical method. Finally, a multiple model predictive controller is designed, and the computational implementation of the multiple model predictive controller is addressed with the auxiliary vectors. Furthermore, a continuous stirred tank reactor (CSTR) is used to confirm the effectiveness of the developed method as well as compare with other operating space decomposition methods.Processes 2020, 8, 215 2 of 13 the heterogeneous multiple model have the capacity to fit the system complexity in each operating space with different dimensions of sub-models. Hence, this kind of multiple model has much more flexibility and generality than a homogeneous multiple model, having more extensive prospect. The heterogeneous model was initially proposed by Filev [23], and different heterogenous model structures, such as the local model network [24][25][26][27] and decoupled multiple model [28,29], have been applied to nonlinear system modeling, control, optimization, etc. [30,31].There are two problems in the multiple model approach-operating/state space decomposition and sub-model combination. In decomposition, a nonlinear system decomposes into several sub-models and the operating/state space of the nonlinear system is partitioned into corresponding operating zones. In the usual multiple model approach, researchers used to consider the decomposition and the combination as two independent procedures [32]. The decomposition is first accomplished, and then the combination of sub-models is carried out [33]. Du et al. used the gap metric to decompose the nonlinear system into model bank determination [34]. Song et al. developed a closed loop decomposition method based on an optimal control problem for nonlinear systems [35,36].However, the above-mentioned decomposition/combination method is mostly applied to homogeneous multiple model structures. With the idea of closed loop decomposition and combination methods [34], a control-performance-based partitioning operating space approach in heterogeneous multiple models is proposed. Initially, the heterogeneous multiple model is obtained at given equilibrium points of a nonlinear system, then auxiliary variables are selected to transform a heterogeneous multiple model into a homogeneous multiple model. An optimal problem where decision variables are composed of control input and the boundary condition of sub-m...

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Autonomous Learning Multimodel Systems From Data Streams

Cited by 78 publications

References 40 publications

Autonomous learning multi-model classifier of 0-Order (ALMMo-0)

Autonomous learning multi-model classifier of 0-Order (ALMMo-0)

Cybernetics of the Mind: Learning Individual's Perceptions Autonomously

A Control-Performance-Based Partitioning Operating Space Approach in a Heterogeneous Multiple Model

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